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  • Perspective
  • Published: 18 October 2022

Machine learning for a sustainable energy future

  • Zhenpeng Yao   ORCID: 1 , 2 , 3 , 4   na1 ,
  • Yanwei Lum   ORCID: 5 , 6   na1 ,
  • Andrew Johnston 6   na1 ,
  • Luis Martin Mejia-Mendoza 2 ,
  • Xin Zhou 7 ,
  • Yonggang Wen 7 ,
  • Alán Aspuru-Guzik   ORCID: 2 , 8 ,
  • Edward H. Sargent   ORCID: 6 &
  • Zhi Wei Seh   ORCID: 5  

Nature Reviews Materials volume  8 ,  pages 202–215 ( 2023 ) Cite this article

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  • Energy grids and networks
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Transitioning from fossil fuels to renewable energy sources is a critical global challenge; it demands advances — at the materials, devices and systems levels — for the efficient harvesting, storage, conversion and management of renewable energy. Energy researchers have begun to incorporate machine learning (ML) techniques to accelerate these advances. In this Perspective, we highlight recent advances in ML-driven energy research, outline current and future challenges, and describe what is required to make the best use of ML techniques. We introduce a set of key performance indicators with which to compare the benefits of different ML-accelerated workflows for energy research. We discuss and evaluate the latest advances in applying ML to the development of energy harvesting (photovoltaics), storage (batteries), conversion (electrocatalysis) and management (smart grids). Finally, we offer an overview of potential research areas in the energy field that stand to benefit further from the application of ML.

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The combustion of fossil fuels, used to fulfill approximately 80% of the world’s energy needs, is the largest single source of rising greenhouse gas emissions and global temperature 1 . The increased use of renewable sources of energy, notably solar and wind power, is an economically viable path towards meeting the climate goals of the Paris Agreement 2 . However, the rate at which renewable energy has grown has been outpaced by ever-growing energy demand, and as a result the fraction of total energy produced by renewable sources has remained constant since 2000 (ref. 3 ). It is thus essential to accelerate the transition towards sustainable sources of energy 4 . Achieving this transition requires energy technologies, infrastructure and policies that enable and promote the harvest, storage, conversion and management of renewable energy.

In sustainable energy research, suitable material candidates (such as photovoltaic materials) must first be chosen from the combinatorial space of possible materials, then synthesized at a high enough yield and quality for use in devices (such as solar panels). The time frame of a representative materials discovery process is 15–20 years 5 , 6 , leaving considerable room for improvement. Furthermore, the devices have to be optimized for robustness and reproducibility to be incorporated into energy systems (such as in solar farms) 7 , where management of energy usage and generation patterns is needed to further guarantee commercial success.

Here we explore the extent to which machine learning (ML) techniques can help to address many of these challenges 8 , 9 , 10 . ML models can be used to predict specific properties of new materials without the need for costly characterization; they can generate new material structures with desired properties; they can understand patterns in renewable energy usage and generation; and they can help to inform energy policy by optimizing energy management at both device and grid levels.

In this Perspective, we introduce Acc(X)eleration Performance Indicators (XPIs), which can be used to measure the effectiveness of platforms developed for accelerated energy materials discovery. Next, we discuss closed-loop ML frameworks and evaluate the latest advances in applying ML to the development of energy harvesting, storage and conversion technologies, as well as the integration of ML into a smart power grid. Finally, we offer an overview of energy research areas that stand to benefit further from ML.

Performance indicators

Because many reports discuss ML-accelerated approaches for materials discovery and energy systems management, we posit that there should be a consistent baseline from which these reports can be compared. For energy systems management, performance indicators at the device, plant and grid levels have been reported 11 , 12 , yet there are no equivalent counterparts for accelerated materials discovery.

The primary goal in materials discovery is to develop efficient materials that are ready for commercialization. The commercialization of a new material requires intensive research efforts that can span up to two decades: the goal of every accelerated approach should be to accomplish commercialization an order-of-magnitude faster. The materials science field can benefit from studying the case of vaccine development. Historically, new vaccines take 10 years from conception to market 13 . However, after the start of the COVID-19 pandemic, several companies were able to develop and begin releasing vaccines in less than a year. This achievement was in part due to an unprecedented global research intensity, but also to a shift in the technology: after a technological breakthrough in 2008, the cost of sequencing DNA began decreasing exponentially 14 , 15 , enabling researchers to screen orders-of-magnitude more vaccines than was previously possible.

ML for energy technologies has much in common with ML for other fields like biomedicine, sharing the same methodology and principles. However, in practice, ML models for different technologies are exposed to additional unique requirements. For example, ML models for medical applications usually have complex structures that take into account regulatory oversight and ensure the safe development, use and monitoring of systems, which usually does not happen in the energy field 16 . Moreover, data availability varies substantially from field to field; biomedical researchers can work with a relatively large amount of data that energy researchers usually lack. This limited data accessibility can constrain the usage of sophisticated ML models (such as deep learning models) in the energy field. However, adaptation has been quick in all energy subfields, with a rapidly increased number of groups recognizing the importance of statistical methods and starting to use them for various problems. We posit that the use of high-throughput experimentation and ML in materials discovery workflows can result in breakthroughs in accelerating development, but the field first needs a set of metrics with which ML models can be evaluated and compared.

Accelerated materials discovery methods should be judged based on the time it takes for a new material to be commercialized. We recognize that this is not a useful metric for new platforms, nor is it one that can be used to decide quickly which platform is best suited for a particular scenario. We therefore propose here XPIs that new materials discovery platforms should report.

Acceleration factor of new materials, XPI-1

This XPI is evaluated by dividing the number of new materials that are synthesized and characterized per unit time with the accelerated platform by the number of materials that are synthesized and characterized with traditional methods. For example, an acceleration factor of ten means that for a given time period, the accelerated platform can evaluate ten times more materials than a traditional platform. For materials with multiple target properties, researchers should report the rate-limiting acceleration factor.

Number of new materials with threshold performance, XPI-2

This XPI tracks the number of new materials discovered with an accelerated platform that have a performance greater than the baseline value. The selection of this baseline value is critical: it should be something that fairly captures the standard to which new materials need to be compared. As an example, an accelerated platform that seeks to discover new perovskite solar cell materials should track the number of devices made with new materials that have a better performance than the best existing solar cell 17 .

Performance of best material over time, XPI-3

This XPI tracks the absolute performance — whether it is Faradaic efficiency, power conversion efficiency or other — of the best material as a function of time. For the accelerated framework, the evolution of the performance should increase faster than the performance obtained by traditional methods 18 .

Repeatability and reproducibility of new materials, XPI-4

This XPI seeks to ensure that the new materials discovered are consistent and repeatable: this is a key consideration to screen out materials that would fail at the commercialization stage. The performance of a new material should not vary by more than x % of its mean value (where x is the standard error): if it does, this material should not be included in either XPI-2 (number of new materials with threshold performance) or XPI-3 (performance of best material over time).

Human cost of the accelerated platform, XPI-5

This XPI reports the total costs of the accelerated platform. This should include the total number of researcher hours needed to design and order the components for the accelerated system, develop the programming and robotic infrastructure, develop and maintain databases used in the system and maintain and run the accelerated platform. This metric would provide researchers with a realistic estimate of the resources required to adapt an accelerated platform for their own research.

Use of the XPIs

Each of these XPIs can be measured for computational, experimental or integrated accelerated systems. Consistently reporting each of these XPIs as new accelerated platforms are developed will allow researchers to evaluate the growth of these platforms and will provide a consistent metric by which different platforms can be compared. As a demonstration, we applied the XPIs to evaluate the acceleration performance of several typical platforms: Edisonian-like trial-test, robotic photocatalysis development 19 and design of a DNA-encoded-library-based kinase inhibitor 20 (Table  1 ). To obtain a comprehensive performance estimate, we define one overall acceleration score S adhering to the following rules. The dependent acceleration factors (XPI-1 and XPI-2), which function in a synergetic way, are added together to reflect their contribution as a whole. The independent acceleration factors (XPI-3, XPI-4 and XPI-5), which may function in a reduplicated way, are multiplied together to value their contributions respectively. As a result, the overall acceleration score can be calculated as S  = (XPI-1 + XPI-2) × XPI-3 × XPI-4 ÷ XPI-5. As the reference, the Edisonian-like approach has a calculated overall XPIs score of around 1, whereas the most advanced method, the DNA-encoded-library-based drug design, exhibits an overall XPIs score of 10 7 . For the sustainability field, the robotic photocatalysis platform has an overall XPIs score of 10 5 .

For energy systems, the most frequently reported XPI is the acceleration factor, in part because it is deterministic, but also because it is easy to calculate at the end of the development of a workflow. In most cases, we expect that authors report the acceleration factor only after completing the development of the platform. Reporting the other suggested XPIs will provide researchers with a better sense of both the time and human resources required to develop the platform until it is ready for publication. Moving forward, we hope that other researchers adopt the XPIs — or other similar metrics — to allow for fair and consistent comparison between the different methods and algorithms that are used to accelerate materials discovery.

Closed-loop ML for materials discovery

The traditional approach to materials discovery is often Edisonian-like, relying on trial and error to develop materials with specific properties. First, a target application is identified, and a starting pool of possible candidates is selected (Fig.  1a ). The materials are then synthesized and incorporated into a device or system to measure their properties. These results are then used to establish empirical structure–property relationships, which guide the next round of synthesis and testing. This slow process goes through as many iterations as required and each cycle can take several years to complete.

figure 1

a | Traditional Edisonian-like approach, which involves experimental trial and error. b | High-throughput screening approach involving a combination of theory and experiment. c | Machine learning (ML)-driven approach whereby theoretical and experimental results are used to train a ML model for predicting structure–property relationships. d | ML-driven approach for property-directed and automatic exploration of the chemical space using optimization ML (such as genetic algorithms or generative models) that solve the ‘inverse’ design problem.

A computation-driven, high-throughput screening strategy (Fig.  1b ) offers a faster turnaround. To explore the overall vast chemical space (~10 60 possibilities), human intuition and expertise can be used to create a library with a substantial number of materials of interest (~10 4 ). Theoretical calculations are carried out on these candidates and the top performers (~10 2 candidates) are then experimentally verified. With luck, the material with the desired functionality is ‘discovered’. Otherwise, this process is repeated in another region of the chemical space. This approach can still be very time-consuming and computationally expensive and can only sample a small region of the chemical space.

ML can substantially increase the chemical space sampled, without costing extra time and effort. ML is data-driven, screening datasets to detect patterns, which are the physical laws that govern the system. In this case, these laws correspond to materials structure–property relationships. This workflow involves high-throughput virtual screening (Fig.  1c ) and begins by selecting a larger region (~10 6 ) of the chemical space of possibilities using human intuition and expertise. Theoretical calculations are carried out on a representative subset (~10 4 candidates) and the results are used for training a discriminative ML model. The model can then be used to make predictions on the other candidates in the overall selected chemical space 9 . The top ~10 2 candidates are experimentally verified, and the results are used to improve the predictive capabilities of the model in an iterative loop. If the desired material is not ‘discovered’, the process is repeated on another region of the chemical space.

An improvement on the previous approaches is a framework that requires limited human intuition or expertise to direct the chemical space search: the automated virtual screening approach (Fig.  1d ). To begin with, a region of the chemical space is picked at random to initiate the process. Thereafter, this process is similar to the previous approach, except that the computational and experimental data is also used to train a generative learning model. This generative model solves the ‘inverse’ problem: given a required property, the goal is to predict an ideal structure and composition in the chemical space. This enables a directed, automated search of the chemical space, towards the goal of ‘discovering’ the ideal material 8 .

ML for energy

ML has so far been used to accelerate the development of materials and devices for energy harvesting (photovoltaics), storage (batteries) and conversion (electrocatalysis), as well as to optimize power grids. Besides all the examples discussed here, we summarize the essential concepts in ML (Box  1 ), the grand challenges in sustainable materials research (Box  2 ) and the details of key studies (Table  2 ).

Box 1 Essential concepts in ML

With the availability of large datasets 122 , 125 and increased computing power, various machine learning (ML) algorithms have been developed to solve diverse problems in energy. Below, we provide a brief overview of the types of problem that ML can solve in energy technology, and we then summarize the status of ML-driven energy research. More detailed information about the nuts and bolts of ML techniques can be found in previous reviews 173 , 174 , 175 .

Property prediction

Supervised learning models are predictive (or discriminative) models that are given a datapoint x , and seek to predict a property y (for example, the bandgap 27 ) after being trained on a labelled dataset. The property y can be either continuous or discrete. These models have been used to aid or even replace physical simulations or measurements under certain circumstances 176 , 177 .

Generative materials design

Unsupervised learning models are generative models that can generate or output new examples x ′ (such as new molecules 104 ) after being trained on an unlabelled dataset. This generation of new examples can be further enhanced with additional information (physical properties) to condition or bias the generative process, allowing the models to generate examples with improved properties and leading to the property-to-structure approach called inverse design 52 , 178 .

Self-driving laboratories

Self-driving or autonomous laboratories 19 use ML models to plan and perform experiments, including the automation of retrosynthesis analysis (such as in reinforcement-learning-aided synthesis planning 124 , 179 ), prediction of reaction products (such as in convolutional neural networks (CNNs) for reaction prediction 137 , 138 ) and reaction condition optimization (such as in robotic workflows optimized by active learning 19 , 160 , 180 , 181 , 182 , 183 ). Self-driving laboratories, which use active learning for iterating through rounds of synthesis and measurements, are a key component in the closed-loop inverse design 52 .

Aiding characterization

ML models have been used to aid the quantitative or qualitative analysis of experimental observations and measurements, including assisting in the determination of crystal structure from transmission electron microscopy images 184 , identifying coordination environment 81 and structural transition 83 from X-ray absorption spectroscopy and inferring crystal symmetry from electron diffraction 176 .

Accelerating theoretical computations

ML models can enable otherwise intractable simulations by reducing the computational cost (processor core amount and time) for systems with increased length and timescales 69 , 70 and providing potentials and functionals for complex interactions 68 .

Optimizing system management

ML models can aid the management of energy systems at the device or grid power level by predicting lifetimes (such as battery life 43 , 44 ), adapting to new loads (such as in long short-term memory for building load prediction 95 ) and optimizing performance (such as in reinforcement learning for smart grid control 94 ).

Box 2 Grand challenges in energy materials research


Discover non-toxic (Pd- and Cd-free) materials with good optoelectronic properties

Identify and minimize materials defects in light-absorbing materials

Design effective recombination-layer materials for tandem solar cells

Develop materials design strategies for long-term operational stability 125

Develop (hole/electron) transport materials with high carrier mobility 125

Optimize cell structure for maximum light absorption and minimum use of active materials

Tune materials bandgaps for optimal solar-harvesting performance under complex operation conditions 21 , 22

Develop Earth-abundant cathode materials (Co-free) with high reversibility and charge capacity 4

Design electrolytes with wider electrochemical windows and high conductivity 4

Identify electrolyte systems to boost battery performance and lifetime 4

Discover new molecules for redox flow batteries with suitable voltage 4

Understand correlation between defect growth in battery materials and overall degradation process of battery components

Tune operando (dis)charging protocol for minimized capacity loss, (dis)charging rate and optimal battery life under diversified conditions 7 , 53

Design materials with optimal adsorption energy for maximized catalytic activity 60 , 61

Identify and study active sites on catalytic materials 58

Engineer catalytic materials for extended durability 58 , 60 , 61

Identify a fuller set of materials descriptors that relate to catalytic activity 60 , 61

Engineer multiple catalytic functionalities into the same material 60 , 61

Design multiscale electrode structures for optimized catalytic activity

Correlate atomistic contamination and growth of catalyst particles with electrode degradation process

Tune operando (dis)charging protocol for minimized capacity loss and optimal cell life

ML is accelerating the discovery of new optoelectronic materials and devices for photovoltaics, but major challenges are still associated with each step.

Photovoltaics materials discovery

One materials class for which ML has proved particularly effective is perovskites, because these materials have a vast chemical space from which the constituents may be chosen. Early representations of perovskite materials for ML were atomic-feature representations, in which each structure is encoded as a fixed-length vector comprised of an average of certain atomic properties of the atoms in the crystal structure 21 , 22 . A similar technique was used to predict new lead-free perovskite materials with the proper bandgap for solar cells 23 (Fig.  2a ). These representations allowed for high accuracy but did not account for any spatial relation between atoms 24 , 25 . Materials systems can also be represented as images 26 or as graphs 27 , enabling the treatment of systems with diverse number of atoms. The latter representation is particularly compelling, as perovskites, particularly organic–inorganic perovskites, have crystal structures that incorporate a varying number of atoms, and the organic molecules can vary in size.

figure 2

a | Energy harvesting 23 . b | Energy storage 38 . c | Energy conversion 76 . d | Energy management 93 . ICSD, Inorganic Crystal Structure Database; ML, machine learning.

Although bandgap prediction is an important first step, this parameter alone is not sufficient to indicate a useful optoelectronic material; other parameters, including electronic defect density and stability, are equally important. Defect energies are addressable with computational methods, but the calculation of defects in structures is extremely computationally expensive, which inhibits the generation of a dataset of defect energies from which an ML model can be trained. To expedite the high-throughput calculation of defect energies, a Python toolkit has been developed 28 that will be pivotal in building a database of defect energies in semiconductors. Researchers can then use ML to predict both the formation energy of defects and the energy levels of these defects. This knowledge will ensure that the materials selected from high-throughput screening will not only have the correct bandgap but will also either be defect-tolerant or defect-resistant, finding use in commercial optoelectronic devices.

Even without access to a large dataset of experimental results, ML can accelerate the discovery of optoelectronic materials. Using a self-driving laboratory approach, the number of experiments required to optimize an organic solar cell can be reduced from 500 to just 60 (ref. 29 ). This robotic synthesis method accelerates the learning rate of the ML models and drastically reduces the cost of the chemicals needed to run the optimization.

Solar device structure and fabrication

Photovoltaic devices require optimization of layers other than the active layer to maximize performance. One component is the top transparent conductive layer, which needs to have both high optical transparency and high electronic conductivity 30 , 31 . A genetic algorithm that optimized the topology of a light-trapping structure enabled a broadband absorption efficiency of 48.1%, which represents a more than threefold increase over the Yablonovitch limit, the 4 n 2 factor (where n is the refractive index of the material) theoretical limit for light trapping in photovoltaics 32 .

A universal standard irradiance spectrum is usually used by researchers to determine optimal bandgaps for solar cell operation 33 . However, actual solar irradiance fluctuates based on factors such as the position of the Sun, atmospheric phenomena and the season. ML can reduce yearly spectral sets into a few characteristic spectra 33 , allowing for the calculation of optimal bandgaps for real-world conditions.

To optimize device fabrication, a CNN was used to predict the current–voltage characteristics of as-cut Si wafers based on their photoluminescence images 34 . Additionally, an artificial neural network was used to predict the contact resistance of metallic front contacts for Si solar cells, which is critical for the manufacturing process 35 .

Although successful, these studies appear to be limited to optimizing structures and processes that are already well established. We suggest that, in future work, ML could be used to augment simulations, such as the multiphysics models for solar cells. Design of device architecture could begin from such simulation models, coupled with ML in an iterative process to quickly optimize design and reduce computational time and cost. In addition, optimal conditions for the scaling-up of device area and fabrication processes are likely to be very different from those for laboratory-scale demonstrations. However, determining these optimal conditions could be expensive in terms of materials cost and time, owing to the need to construct much larger devices. In this regard, ML, together with the strategic design of experiments, could greatly accelerate the optimization of process conditions (such as the annealing temperatures and solvent choice).

Electrochemical energy storage

Electrochemical energy storage is an essential component in applications such as electric vehicles, consumer electronics and stationary power stations. State-of-the-art electrochemical energy storage solutions have varying efficacy in different applications: for example, lithium-ion batteries exhibit excellent energy density and are widely used in electronics and electric vehicles, whereas redox flow batteries have drawn substantial attention for use in stationary power storage. ML approaches have been widely employed in the field of batteries, including for the discovery of new materials such as solid-state ion conductors 36 , 37 , 38 (Fig.  2b ) and redox active electrolytes for redox flow batteries 39 . ML has also aided battery management, for example, through state-of-charge determination 40 , state-of-health evaluation 41 , 42 and remaining-life prediction 43 , 44 .

Electrode and electrolyte materials design

Layered oxide materials, such as LiCoO 2 or LiNi x Mn y Co 1- x - y O 2 , have been used extensively as cathode materials for alkali metal-ion (Li/Na/K) batteries. However, developing new Li-ion battery materials with higher operating voltages, enhanced energy densities and longer lifetimes is of paramount interest. So far, universal design principles for new battery materials remain undefined, and hence different approaches have been explored. Data from the Materials Project have been used to model the electrode voltage profile diagrams for different materials in alkali metal-ion batteries (Na and K) 45 , leading to the proposition of 5,000 different electrode materials with appropriate moderate voltages. ML was also employed to screen 12,000 candidates for solid Li-ion batteries, resulting in the discovery of ten new Li-ion conducting materials 46 , 47 .

Flow batteries consist of active materials dissolved in electrolytes that flow into a cell with electrodes that facilitate redox reactions. Organic flow batteries are of particular interest. In flow batteries, the solubility of the active material in the electrolyte and the charge/discharge stability dictate performance. ML methods have explored the chemical space to find suitable electrolytes for organic redox flow batteries 48 , 49 . Furthermore, a multi-kernel-ridge regression method accelerated the discovery of active organic molecules using multiple feature training 48 . This method also helped in predicting the solubility dependence of anthraquinone molecules with different numbers and combinations of sulfonic and hydroxyl groups on pH. Future opportunities lie in the exploration of large combinatorial spaces for the inverse design of high-entropy electrodes 50 and high-voltage electrolytes 51 . To this end, deep generative models can assist the discovery of new materials based on the simplified molecular input line entry system (SMILES) representation of molecules 52 .

Battery device and stack management

A combination of mechanistic and semi-empirical models is currently used to estimate capacity and power loss in lithium-ion batteries. However, the models are applicable only to specific failure mechanisms or situations and cannot predict the lifetimes of batteries at the early stages of usage. By contrast, mechanism-agnostic models based on ML can accurately predict battery cycle life, even at an early stage of a battery’s life 43 . A combined early-prediction and Bayesian optimization model has been used to rapidly identify the optimal charging protocol with the longest cycle life 44 . ML can be used to accelerate the optimization of lithium-ion batteries for longer lifetimes 53 , but it remains to be seen whether these models can be generalized to different battery chemistries 54 .

ML methods can also predict important properties of battery storage facilities. A neural network was used to predict the charge/discharge profiles in two types of stationary battery systems, lithium iron phosphate and vanadium redox flow batteries 55 . Battery power management techniques must also consider the uncertainty and variability that arise from both the environment and the application. An iterative Q -learning ( reinforcement learning ) method was also designed for battery management and control in smart residential environments 56 . Given the residential load and the real-time electricity rate, the method is effective at optimizing battery charging/discharging/idle cycles. Discriminative neural network-based models can also optimize battery usage in electric vehicles 57 .

Although ML is able to predict the lifetime of batteries, the underlying degradation mechanisms are difficult to identify and correlate to the state of health and lifetime. To this end, incorporation of domain knowledge into a hybrid physics-based ML model can provide insight and reduce overfitting 53 . However, incorporating the physics of battery degradation processes into a hybrid model remains challenging; representation of electrode materials that encode both compositional and structural information is far from trivial. Validation of these models also requires the development of operando characterization techniques, such as liquid-phase transmission electron microscopy and ambient-pressure X-ray absorption spectroscopy (XAS), that reflect true operating conditions as closely as possible 54 . Ideally, these characterization techniques should be carried out in a high-throughput manner, using automated sample changers, for example, in order to generate large datasets for ML.


Electrocatalysis enables the conversion of simple feedstocks (such as water, carbon dioxide and nitrogen) into valuable chemicals and/or fuels (such as hydrogen, hydrocarbons and ammonia), using renewable energy as an input 58 . The reverse reactions are also possible in a fuel cell, and hydrogen can be consumed to produce electricity 59 . Active and selective electrocatalysts must be developed to improve the efficiency of these reactions 60 , 61 . ML has been used to accelerate electrocatalyst development and device optimization.

Electrocatalyst materials discovery

The most common descriptor of catalytic activity is the adsorption energy of intermediates on a catalyst 61 , 62 . Although these adsorption energies can be calculated using density functional theory (DFT), catalysts possess multiple surface binding sites, each with different adsorption energies 63 . The number of possible sites increases dramatically if alloys are considered, and thus becomes intractable with conventional means 64 .

DFT calculations are critical for the search of electrocatalytic materials 65 and efforts have been made to accelerate the calculations and to reduce their computational cost by using surrogate ML models 66 , 67 , 68 , 69 . Complex reaction mechanisms involving hundreds of possible species and intermediates can also be simplified using ML, with a surrogate model predicting the most important reaction steps and deducing the most likely reaction pathways 70 . ML can also be used to screen for active sites across a random, disordered nanoparticle surface 71 , 72 . DFT calculations are performed on only a few representative sites, which are then used to train a neural network to predict the adsorption energies of all active sites.

Catalyst development can benefit from high-throughput systems for catalyst synthesis and performance evaluation 73 , 74 . An automatic ML-driven framework was developed to screen a large intermetallic chemical space for CO 2 reduction and H 2 evolution 75 . The model predicted the adsorption energy of new intermetallic systems and DFT was automatically performed on the most promising candidates to verify the predictions. This process went on iteratively in a closed feedback loop. 131 intermetallic surfaces across 54 alloys were ultimately identified as promising candidates for CO 2 reduction. Experimental validation 76 with Cu–Al catalysts yielded an unprecedented Faradaic efficiency of 80% towards ethylene at a high current density of 400 mA cm – 2 (Fig.  2c ).

Because of the large number of properties that electrocatalysts may possess (such as shape, size and composition), it is difficult to do data mining on the literature 77 . Electrocatalyst structures are complex and difficult to characterize completely; as a result, many properties may not be fully characterized by research groups in their publications. To avoid situations in which potentially promising compositions perform poorly as a result of non-ideal synthesis or testing conditions, other factors (such as current density, particle size and pH value) that affect the electrocatalyst performance must be kept consistent. New approaches such as carbothermal shock synthesis 78 , 79 may be a promising avenue, owing to its propensity to generate uniformly sized and shaped alloy nanoparticles, regardless of composition.

XAS is a powerful technique, especially for in situ measurements, and has been widely employed to gain crucial insight into the nature of active sites and changes in the electrocatalyst over time 80 . Because the data analysis relies heavily on human experience and expertise, there has been interest in developing ML tools for interpreting XAS data 81 . Improved random forest models can predict the Bader charge (a good approximation of the total electronic charge of an atom) and nearest-neighbour distances, crucial factors that influence the catalytic properties of the material 82 . The extended X-ray absorption fine structure (EXAFS) region of XAS spectra is known to contain information on bonding environments and coordination numbers. Neural networks can be used to automatically interpret EXAFS data 83 , permitting the identification of the structure of bimetallic nanoparticles using experimental XAS data, for example 84 . Raman and infrared spectroscopy are also important tools for the mechanistic understanding of electrocatalysis. Together with explainable artificial intelligence (AI), which can relate the results to underlying physics, these analyses could be used to discover descriptors hidden in spectra that could lead to new breakthroughs in electrocatalyst discovery and optimization.

Fuel cell and electrolyser device management

A fuel cell is an electrochemical device that can be used to convert the chemical energy of a fuel (such as hydrogen) into electrical energy. An electrolyser transforms electrical energy into chemical energy (such as in water splitting to generate hydrogen). ML has been used to optimize and manage their performance, predict degradation and device lifetime as well as detect and diagnose faults. Using a hybrid method consisting of an extreme learning machine, genetic algorithms and wavelet analysis, the degradation in proton-exchange membrane fuel cells has been predicted 85 , 86 . Electrochemical impedance measurements used as input for an artificial neural network have enabled fault detection and isolation in a high-temperature stack of proton-exchange membrane fuel cells 87 , 88 .

ML approaches can also be employed to diagnose faults, such as fuel and air leakage issues, in solid oxide fuel cell stacks. Artificial neural networks can predict the performance of solid oxide fuel cells under different operating conditions 89 . In addition, ML has been applied to optimize the performance of solid oxide electrolysers, for CO 2 /H 2 O reduction 90 , and chloralkali electrolysers 91 .

In the future, the use of ML for fuel cells could be combined with multiscale modelling to improve their design, for example to minimize Ohmic losses and optimize catalyst loading. For practical applications, fuel cells may be subject to fluctuations in energy output requirements (for example, when used in vehicles). ML models could be used to determine the effects of such fluctuations on the long-term durability and performance of fuel cells, similar to what has been done for predicting the state of health and lifetime for batteries. Furthermore, it remains to be seen whether the ML techniques for fuel cells can be easily generalized to electrolysers and vice versa, using transfer learning for example, given that they are essentially reactions in reverse.

Smart power grids

A power grid is responsible for delivering electrical energy from producers (such as power plants and solar farms) to consumers (such as homes and offices). However, energy fluctuations from intermittent renewable energy generators can render the grid vulnerable 92 . ML algorithms can be used to optimize the automatic generation control of power grids, which controls the power output of multiple generators in an energy system. For example, when a relaxed deep learning model was used as a unified timescale controller for the automatic generation control unit, the total operational cost was reduced by up to 80% compared with traditional heuristic control strategies 93 (Fig.  2d ). A smart generation control strategy based on multi-agent reinforcement learning was found to improve the control performance by around 10% compared with other ML algorithms 94 .

Accurate demand and load prediction can support decision-making operations in energy systems for proper load scheduling and power allocation. Multiple ML methods have been proposed to precisely predict the demand load: for example, long short-term memory was used to successfully and accurately predict hourly building load 95 . Short-term load forecasting of diverse customers (such as retail businesses) using a deep neural network and cross-building energy demand forecasting using a deep belief network have also been demonstrated effectively 96 , 97 .

Demand-side management consists of a set of mechanisms that shape consumer electricity consumption by dynamically adjusting the price of electricity. These include reducing (peak shaving), increasing (load growth) and rescheduling (load shifting) the energy demand, which allows for flexible balancing of renewable electricity generation and load 98 . A reinforcement-learning-based algorithm resulted in substantial cost reduction for both the service provider and customer 99 . A decentralized learning-based residential demand scheduling technique successfully shifted up to 35% of the energy demand to periods of high wind availability, substantially saving power costs compared with the unscheduled energy demand scenario 100 . Load forecasting using a multi-agent approach integrates load prediction with reinforcement learning algorithms to shift energy usage (for example, to different electrical devices in a household) for its optimization 101 . This approach reduced peak usage by more than 30% and increased off-peak usage by 50%, reducing the cost and energy losses associated with energy storage.

Opportunities for ML in renewable energy

ML provides the opportunity to enable substantial further advances in different areas of the energy materials field, which share similar materials-related challenges (Fig.  3 ). There are also grand challenges for ML application in smart grid and policy optimization.

figure 3

a | Energy materials present additional modelling challenges. Machine learning (ML) could help in the representation of structurally complex structures, which can include disordering, dislocations and amorphous phases. b | Flexible models that scale efficiently with varied dataset sizes are in demand, and ML could help to develop robust predictive models. The yellow dots stand for the addition of unreliable datasets that could harm the prediction accuracy of the ML model. c | Synthesis route prediction remains to be solved for the design of a novel material. In the ternary phase diagram, the dots stand for the stable compounds in that corresponding phase space and the red dot for the targeted compound. Two possible synthesis pathways are compared for a single compound. The score obtained would reflect the complexity, cost and so on of one synthesis pathway. d | ML-aided phase degradation prediction could boost the development of materials with enhanced cyclability. The shaded region represents the rocksalt phase, which grows inside the layered phase. The arrow marks the growth direction. e | The use of ML models could help in optimizing energy generation and energy consumption. Automating the decision-making processes associated with dynamic power supplies using ML will make the power distribution more efficient. f | Energy policy is the manner in which an entity (for example, a government) addresses its energy issues, including conversion, distribution and utilization, where ML could be used to optimize the corresponding economy.

Materials with novel geometries

A ML representation is effective when it captures the inherent properties of the system (such as its physical symmetries) and can be utilized in downstream ancillary tasks, such as transfer learning to new predictive tasks, building new knowledge using visualization or attribution and generating similar data distributions with generative models 102 .

For materials, the inputs are molecules or crystal structures whose physical properties are modelled by the Schrödinger equation. Designing a general representation of materials that reflects these properties is an ongoing research problem. For molecular systems, several representations have been used successfully, including fingerprints 103 , SMILES 104 , self-referencing embedded strings (SELFIES) 105 and graphs 106 , 107 , 108 . Representing crystalline materials has the added complexity of needing to incorporate periodicity in the representation. Methods like the smooth overlap of atomic positions 109 , Voronoi tessellation 110 , 111 , diffraction images 112 , multi-perspective fingerprints 113 and graph-based algorithms 27 , 114 have been suggested, but typically lack the capability for structure reconstruction.

Complex structural systems found in energy materials present additional modelling challenges (Fig.  3a ): a large number of atoms (such as in reticular frameworks or polymers), specific symmetries (such as in molecules with a particular space group and for reticular frameworks belonging to a certain topology), atomic disordering, partial occupancy, or amorphous phases (leading to an enormous combinatorial space), defects and dislocations (such as interfaces and grain boundaries) and low-dimensionality materials (as in nanoparticles). Reduction approximations alleviate the first issue (using, for example, RFcode for reticular framework representation) 8 , but the remaining several problems warrant intensive future research efforts.

Self- supervised learning , which seeks to lever large amounts of synthetic labels and tasks to continue learning without experimental labels 115 , multi-task learning 116 , in which multiple material properties can be modelled jointly to exploit correlation structure between properties, and meta-learning 117 , which looks at strategies that allow models to perform better in new datasets or in out-of-distribution data, all offer avenues to build better representations. On the modelling front, new advances in attention mechanisms 118 , 119 , graph neural networks 120 and equivariant neural networks 121 expand our range of tools with which to model interactions and expected symmetries.

Robust predictive models

Predictive models are the first step when building a pipeline that seeks materials with desired properties. A key component for building these models is training data; more data will often translate into better-performing models, which in turn will translate into better accuracy in the prediction of new materials. Deep learning models tend to scale more favourably with dataset size than traditional ML approaches (such as random forests). Dataset quality is also essential. However, experiments are usually conducted under diverse conditions with large variation in untracked variables (Fig.  3b ). Additionally, public datasets are more likely to suffer from publication bias, because negative results are less likely to be published even though they are just as important as positive results when training statistical models 122 .

Addressing these issues require transparency and standardization of the experimental data reported in the literature. Text and natural language processing strategies could then be employed to extract data from the literature 77 . Data should be reported with the belief that it will eventually be consolidated in a database, such as the MatD3 database 123 . Autonomous laboratory techniques will help to address this issue 19 , 124 . Structured property databases such as the Materials Project 122 and the Harvard Clean Energy Project 125 can also provide a large amount of data. Additionally, different energy fields — energy storage, harvesting and conversion — should converge upon a standard and uniform way to report data. This standard should be continuously updated; as researchers continue to learn about the systems they are studying, conditions that were previously thought to be unimportant will become relevant.

New modelling approaches that work in low-data regimes, such as data-efficient models, dataset-building strategies (active sampling) 126 and data-augmentation techniques, are also important 127 . Uncertainty quantification , data efficiency, interpretability and regularization are important considerations that improve the robustness of ML models. These considerations relate to the notion of generalizability: predictions should generalize to a new class of materials that is out of the distribution of the original dataset. Researchers can attempt to model how far away new data points are from the training set 128 or the variability in predicted labels with uncertainty quantification 129 . Neural networks are a flexible model class, and often models can be underspecified 130 . Incorporating regularization, inductive biases or priors can boost the credibility of a model. Another way to create trustable models could be to enhance the interpretability of ML algorithms by deriving feature relevance and scoring their importance 131 . This strategy could help to identify potential chemically meaningful features and form a starting point for understanding latent factors that dominate material properties. These techniques can also identify the presence of model bias and overfitting, as well as improving generalization and performance 132 , 133 , 134 .

Stable and synthesizable new materials

The formation energy of a compound is used to estimate its stability and synthesizability 135 , 136 . Although negative values usually correspond to stable or synthesizable compounds, slightly positive formation energies below a limit lead to metastable phases with unclear synthesizability 137 , 138 . This is more apparent when investigating unexplored chemical spaces with undetermined equilibrium ground states; yet often the metastable phases exhibit superior properties, as seen in photovoltaics 136 , 139 and ion conductors 140 , for example. It is thus of interest to develop a method to evaluate the synthesizability of metastable phases (Fig.  3c ). Instead of estimating the probability that a particular phase can be synthesized, one can instead evaluate its synthetic complexity using ML. In organic chemistry, synthesis complexity is evaluated according to the accessibility of the phases’ synthesis route 141 or precedent reaction knowledge 142 . Similar methodologies can be applied to the inorganic field with the ongoing design of automated synthesis-planning algorithms for inorganic materials 143 , 144 .

Synthesis and evaluation of a new material alone does not ensure that material will make it to market; material stability is a crucial property that takes a long time to evaluate. Degradation is a generally complex process that occurs through the loss of active matter or growth of inactive phases (such as the rocksalt phases formed in layered Li-ion battery electrodes 145 (Fig.  3d ) or the Pt particle agglomeration in fuel cells 146 ) and/or propagation of defects (such as cracks in cycled battery electrode 147 ). Microscopies such as electron microscopy 148 and simulations such as continuum mechanics modelling 149 are often used to investigate growth and propagation dynamics (that is, phase boundary and defect surface movements versus time). However, these techniques are usually expensive and do not allow rapid degradation prediction. Deep learning techniques such as convolutional neural networks and recurrent neural networks may be able to predict the phase boundary and/or defect pattern evolution under certain conditions after proper training 150 . Similar models can then be built to understand multiple degradation phenomena and aid the design of materials with improved cycle life.

Optimized smart power grids

A promising prospect of ML in smart grids is automating the decision-making processes that are associated with dynamic power supplies to distribute power most efficiently (Fig.  3e ). Practical deployment of ML technologies into physical systems remains difficult because of data scarcity and the risk-averse mindset of policymakers. The collection of and access to large amounts of diverse data is challenging owing to high cost, long delays and concerns over compliance and security 151 . For instance, to capture the variation of renewable resources owing to peak or off-peak and seasonal attributes, long-term data collections are implemented for periods of 24 hours to several years 152 . Furthermore, although ML algorithms are ideally supposed to account for all uncertainties and unpredictable situations in energy systems, the risk-adverse mindset in the energy management industry means that implementation still relies on human decision-making 153 .

An ML-based framework that involves a digital twin of the physical system can address these problems 154 , 155 . The digital twin represents the digitalized cyber models of the physical system and can be constructed from physical laws and/or ML models trained using data sampled from the physical system. This approach aims to accurately simulate the dynamics of the physical system, enabling relatively fast generation of large amounts of high-quality synthetic data at low cost. Notably, because ML model training and validation is performed on the digital twin, there is no risk to the actual physical system. Based on the prediction results, suitable actions can be suggested and then implemented in the physical system to ensure stability and/or improve system operation.

Policy optimization

Finally, research is generally focused on one narrow aspect of a larger problem; we argue that energy research needs a more integrated approach 156 (Fig.  3f ). Energy policy is the manner in which an entity, such as the government, addresses its energy issues, including conversion, distribution and utilization. ML has been used in the fields of energy economics finance for performance diagnostics (such as for oil wells), energy generation (such as wind power) and consumption (such as power load) forecasts and system lifespan (such as battery cell life) and failure (such as grid outage) prediction 157 . They have also been used for energy policy analysis and evaluation (for example, for estimating energy savings). A natural extension of ML models is to use them for policy optimization 158 , 159 , a concept that has not yet seen widespread use. We posit that the best energy policies — including the deployment of the newly discovered materials — can be improved and augmented with ML and should be discussed in research reporting accelerated energy technology platforms.


To summarize, ML has the potential to enable breakthroughs in the development and deployment of sustainable energy techniques. There have been remarkable achievements in many areas of energy technology, from materials design and device management to system deployment. ML is particularly well suited to discovering new materials, and researchers in the field are expecting ML to bring up new materials that may revolutionize the energy industry. The field is still nascent, but there is conclusive evidence that ML is at least able to expose the same trends that human researchers have noticed over decades of research. The ML field itself is still seeing rapid development, with new methodologies being reported daily. It will take time to develop and adopt these methodologies to solve specific problems in materials science. We believe that for ML to truly accelerate the deployment of sustainable energy, it should be deployed as a tool, similar to a synthesis procedure, characterization equipment or control apparatus. Researchers using ML to accelerate energy technology discovery should judge the success of the method primarily on the advances it enables. To this end, we have proposed the XPIs and some areas in which we hope to see ML deployed.

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Z.Y. and A.A.-G. were supported as part of the Nanoporous Materials Genome Center by the US Department of Energy, Office of Science, Office of Basic Energy Sciences under award number DE-FG02-17ER16362 and the US Department of Energy, Office of Science — Chicago under award number DE-SC0019300. A.J. was financially supported by Huawei Technologies Canada and the Natural Sciences and Engineering Research Council (NSERC). L.M.M.-M. thanks the support of the Defense Advanced Research Projects Agency under the Accelerated Molecular Discovery Program under cooperative agreement number HR00111920027 dated 1 August 2019. Y.W. acknowledges funding support from the Singapore National Research Foundation under its Green Buildings Innovation Cluster (GBIC award number NRF2015ENC-GBICRD001-012) administered by the Building and Construction Authority, its Green Data Centre Research (GDCR award number NRF2015ENC-GDCR01001-003) administered by the Info-communications Media Development Authority, and its Energy Programme (EP award number NRF2017EWT-EP003-023) administered by the Energy Market Authority of Singapore. A.A.-G. is a Canadian Institute for Advanced Research (CIFAR) Lebovic Fellow. E.H.S. acknowledges funding by the Ontario Ministry of Colleges and Universities (grant ORF-RE08-034), the Natural Sciences and Engineering Research Council (NSERC) of Canada (grant RGPIN-2017-06477), the Canadian Institute for Advanced Research (CIFAR) (grant FS20-154 APPT.2378) and the University of Toronto Connaught Fund (grant GC 2012-13). Z.W.S. acknowledges funding by the Singapore National Research Foundation (NRF-NRFF2017-04).

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These authors contributed equally: Zhenpeng Yao, Yanwei Lum, Andrew Johnston.

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Shanghai Key Laboratory of Hydrogen Science & Center of Hydrogen Science, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

Zhenpeng Yao

Chemical Physics Theory Group, Department of Chemistry and Department of Computer Science, University of Toronto, Toronto, Ontario, Canada

Zhenpeng Yao, Luis Martin Mejia-Mendoza & Alán Aspuru-Guzik

Innovation Center for Future Materials, Zhangjiang Institute for Advanced Study, Shanghai Jiao Tong University, Shanghai, China

State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

Institute of Materials Research and Engineering, Agency for Science, Technology and Research (A*STAR), Innovis, Singapore, Singapore

Yanwei Lum & Zhi Wei Seh

Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario, Canada

Yanwei Lum, Andrew Johnston & Edward H. Sargent

School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

Xin Zhou & Yonggang Wen

Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada

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Innovative Modular Floating Structure for Harvesting Solar Energy in Harsh Marine Environment

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solar energy harvesting research papers

  • Jian Dai 13 ,
  • Zhiyu Jiang 14 ,
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Part of the book series: Lecture Notes in Civil Engineering ((LNCE,volume 465))

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The use of floating structures for harvesting clean solar energy on water bodies has become popular thanks to the technological advancement that has led to more energy- and cost-efficient photovoltaic panels and the availability and environmental benefits of large-scale water bodies. Built on the successful experiences of floating solar farms on inland and coastal waters, research and development activities are now oriented toward floating large-scale solar farms on offshore waters where the space is abundant. At the same time, harsh environmental conditions make it challenging in almost every aspect of the lifecycle, including the design, construction, onsite installation, operation and maintenance, and decommissioning. This paper presents the conceptual development of an innovative soft-connected lattice-structured modular floating solar farm for use in open offshore environments. Technical feasibility assessments involving hydrostatic examinations were carried out to ensure that the proposed concept fulfills the design requirements. Based on these, scaled model tests of both a small array comprising six connected standard floats and a large array of 216 floats were conducted. The experimental study showed that the proposed concept can perform well under both operational conditions and survive extreme conditions with wave heights above 10 m.

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Department of Built Environment, Oslo Metropolitan University, 0166, Oslo, Norway

Department of Engineering Sciences, University of Agder, N-4879, Grimstad, Norway

Zhiyu Jiang

CEHINAV, ETSIN, Universidad Politécnica de Madrid, Madrid, Spain

Simone Saettone & Antonio Souto-Iglesias

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Tomoki Ikoma

Environment Systems, The University of Tokyo, Chiba, Japan

Shigeru Tabeta

Society of Floating Soultions, Singapore, Singapore

Soon Heng Lim

School of Civil Engineering, Building 49, University of Queensland, St. Lucia, Queensland, QLD, Australia

Chien Ming Wang

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Dai, J., Jiang, Z., Saettone, S., Souto-Iglesias, A. (2024). Innovative Modular Floating Structure for Harvesting Solar Energy in Harsh Marine Environment. In: Ikoma, T., Tabeta, S., Lim, S.H., Wang, C.M. (eds) Proceedings of the Third World Conference on Floating Solutions. WCFS 2023. Lecture Notes in Civil Engineering, vol 465. Springer, Singapore.

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Photovoltaic Cell Generations and Current Research Directions for Their Development

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The purpose of this paper is to discuss the different generations of photovoltaic cells and current research directions focusing on their development and manufacturing technologies. The introduction describes the importance of photovoltaics in the context of environmental protection, as well as the elimination of fossil sources. It then focuses on presenting the known generations of photovoltaic cells to date, mainly in terms of the achievable solar-to-electric conversion efficiencies, as well as the technology for their manufacture. In particular, the third generation of photovoltaic cells and recent trends in its field, including multi-junction cells and cells with intermediate energy levels in the forbidden band of silicon, are discussed. We also present the latest developments in photovoltaic cell manufacturing technology, using the fourth-generation graphene-based photovoltaic cells as an example. An extensive review of the world literature led us to the conclusion that, despite the appearance of newer types of photovoltaic cells, silicon cells still have the largest market share, and research into ways to improve their efficiency is still relevant.

1. Introduction

Concerns about climate change and the increase in demand for electricity due to, among other things, an ever-growing population, necessitate efforts to move away from conventional methods of energy production. Rising carbon dioxide levels in the atmosphere caused by the use of fossil fuels is one of the factors causing ongoing climate change. Switching to renewable energy will produce energy with a smaller environmental footprint compared to fossil fuel sources. We are able to harness the full potential of sunlight energy to develop the best possible energy harvesting technologies capable of converting solar energy into electricity [ 1 ].

The currently used solar energy is very marginal—0.015% is used for electricity production, 0.3% for heating, and 11% is used in the natural photosynthesis of biomass. In contrast, about 80–85% of global energy needs are met by fossil fuels. The difficulty with fossil fuels is that their resources are limited and hostile to the environment due to their CO 2 emissions. For instance, for every ton of coal burned, one ton of carbon dioxide is released into the atmosphere. This emitted carbon dioxide is toxic to the environment and is a primary cause of global warming, the greenhouse effect, climate change, and ozone depletion [ 2 ].

The necessity of finding new renewable energy forms is extremely relevant and urgent today. That is why mankind must find alternative sources of energy to provide a clean and sustainable future. Within this context, solar energy is the best option among all alternative renewable energy sources due to its widespread accessibility, universality, and eco-friendly nature [ 3 ].

The most common metric used to evaluate the performance of photovoltaic technologies is conversion efficiency, which expresses the ratio of solar energy input to electrical energy output. The efficiency combines multiple component characteristics of the system, such as short-circuit current, open-circuit voltage, and fill factor, which in turn are dependent upon basic material features and manufacturing defects [ 4 ].

The cost-effectiveness of making a photovoltaic cell and its efficiency depend on the material from which it is made. Much research in this field has been carried out to find the material that is the most efficient and cost-effective for building photovoltaic cells. The specifications for an ideal material for PV solar cells include the following [ 5 ]:

  • The cells are expected to have a band gap between 1.1 and 1.7 eV;
  • Should have a direct band structure;
  • Need to be easily accessible and non-toxic; and
  • Should have high photovoltaic conversion efficiency [ 5 ].

A key problem in the area of photovoltaic cell development is the development of methods to achieve the highest possible efficiency at the lowest possible production cost. Improving the efficiency of solar cells is possible by using effective ways to reduce the internal losses of the cell. There are three basic types of losses: optical, quantum, and electrical, which have different sources of origin. Reducing losses of any kind requires different, often advanced, methods of cell manufacturing and photovoltaic module production. An upper efficiency limit for commercially accessible technologies is determined by the well-known Shockley–Queisser (SQ) limit, taking into account the balance between photogeneration and radiative recombination [ 6 ].

However, the greatest potential lies in the ability to reduce quantum losses, as they are intimately connected with the material properties and internal structure of the cell. Relevant here is the concept of band gap, which defines the minimum required energy of a photon incident onto the cell surface for it to take part in the photovoltaic conversion process. There is a relationship between the efficiency of the cell and the value of the band gap, which in turn is highly dependent on the material from which the photovoltaic cell is made. The basic, commonly used material for solar cells is silicon, which has a band gap value of about 1.12 eV, but by introducing modifications in its crystal structure, the physical properties of the material, especially the band gap width, can be affected [ 7 ].

The dominant loss mechanisms in conventional photovoltaic cells are the inability to absorb photons below the band gap and the thermalization of solar photons with energies above the band gap energy. Third-generation solar cell concepts have been proposed to address these two loss mechanisms in an attempt to improve solar cell performance. These solutions aim to exploit the entire spectrum by incorporating novel mechanisms to create new electron–hole pairs [ 8 ].

Major development potential among these concepts for improving the power generation efficiency of solar cells made of silicon is shown by the idea of cells whose basic feature is an additional intermediate band in the band gap model of silicon. It is located between the conduction band and the valence band, and its function is to allow the absorption of photons with energies below the width of the energy gap, resulting in higher quantum efficiency (a higher number of excited electrons in relation to the number of photons incident onto the surface of the cell) [ 9 ]. Currently, many directions of research development on the introduction of intermediate bands in semiconductors can be identified. One of them is the use of ion implantation, where two methods can be distinguished: introduction of dopants with extremely high concentrations to the substrate of the semiconductor, and implantation of the layer of silicon with high-dose metal ions [ 10 ].

The improvement of solar cell efficiency involves reducing various types of losses affecting the resultant cell efficiency. The National Renewable Energy Laboratory (NREL) runs a compilation of the highest verified research cell conversion efficiencies for different photovoltaic technologies, compiled from 1976 to the present ( Figure 1 ). Cell efficiency results are given for each semiconductor family: multi-junction cells; gallium arsenide single-junction cells; crystalline silicon cells; thin film technologies; emerging photovoltaic technologies. The latest world record for an individual technology is indicated by a flag across the right edge containing the efficiency and technology symbol [ 11 ].

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NREL Best Research-Cell Efficiencies chart [ 11 ].

Photovoltaic cells can be categorized by four main generations: first, second, third, and fourth generation. The details of each are discussed in the next section.

2. Photovoltaic Cell Generations

In the past decade, photovoltaics have become a major contributor to the ongoing energy transition. Advances relating to materials and manufacturing methods have had a significant role behind that development. However, there are still numerous challenges before photovoltaics can provide cleaner and low-cost energy. Research in this direction is focused on efficient photovoltaic devices such as multi-junction cells, graphene or intermediate band gap cells, and printable solar cell materials such as quantum dots [ 12 ].

The primary role of a photovoltaic cell is to receive solar radiation as pure light and transform it into electrical energy in a conversion process called the photovoltaic effect. There are several technologies involved with the manufacturing process of photovoltaic cells, using material modification with different photoelectric conversion efficiencies in the cell components. Due to the emergence of many non-conventional manufacturing methods for fabricating functioning solar cells, photovoltaic technologies can be divided into four major generations, which is shown in Figure 2 [ 13 ].

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Various solar cell types and current developments within this field [ 14 ].

The generations of various photovoltaic cells essentially tell the story of the stages of their past evolution. There are four main categories that are described as the generations of photovoltaic technology for the last few decades, since the invention of solar cells [ 15 ]:

  • First Generation: This category includes photovoltaic cell technologies based on monocrystalline and polycrystalline silicon and gallium arsenide (GaAs).
  • Second Generation: This generation includes the development of first-generation photovoltaic cell technology, as well as the development of thin film photovoltaic cell technology from “microcrystalline silicon (µc-Si) and amorphous silicon (a-Si), copper indium gallium selenide (CIGS) and cadmium telluride/cadmium sulfide (CdTe/CdS) photovoltaic cells”.
  • Third Generation: This generation counts photovoltaic technologies that are based on more recent chemical compounds. In addition, technologies using nanocrystalline “films,” quantum dots, dye-sensitized solar cells, solar cells based on organic polymers, etc., also belong to this generation.
  • Fourth Generation: This generation includes the low flexibility or low cost of thin film polymers along with the durability of “innovative inorganic nanostructures such as metal oxides and metal nanoparticles or organic-based nanomaterials such as graphene, carbon nanotubes and graphene derivatives” [ 15 ].

Examples of solar cell types for each generation along with average efficiencies are shown in Figure 3 .

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Examples of photovoltaic cell efficiencies [ 16 ].

2.1. First Generation of Photovoltaic Cells

Silicon-based PV cells were the first sector of photovoltaics to enter the market, using processing information and raw materials supplied by the industry of microelectronics. Solar cells based on silicon now comprise more than 80% of the world’s installed capacity and have a 90% market share. Due to their relatively high efficiency, they are the most commonly used cells. The first generation of photovoltaic cells includes materials based on thick crystalline layers composed of Si silicon. This generation is based on mono-, poly-, and multicrystalline silicon, as well as single III-V junctions (GaAs) [ 17 , 18 ].

Comparison of first-generation photovoltaic cells [ 18 ]:

  • Solar cells based on monocrystalline silicon (m-si)

Efficiency : 15 ÷ 24%; Band gap : ~1.1 eV; Life span : 25 years; Advantages : Stability, high performance, long service life; Restrictions : High manufacturing cost, more temperature sensitivity, absorption problem, material loss.

  • Solar cells based on polycrystalline silicon (p-si)

Efficiency : 10 ÷ 18%; Band gap : ~1.7 eV; Life span : 14 years; Advantages : Manufacturing procedure is simple, profitable, decreases the waste of silicon, higher absorption compared to m-si; Restrictions : Lower efficiency, higher temperature sensitivity.

  • Solar cells based on GaAs

Efficiency : 28 ÷ 30%; Band gap : ~1.43 eV; Life span : 18 years; Advantages : High stability, lower temperature sensitivity, better absorption than m-si, high efficiency; Restrictions : Extremely expensive [ 18 ].

The first generation concerns p-n junction-based photovoltaic cells, which are mainly represented by mono- or polycrystalline wafer-based silicon photovoltaic cells. Monocrystalline silicon solar cells involve growing Si blocks from small monocrystalline silicon seeds and then cutting them to form monocrystalline silicon wafers, which are fabricated using the Czochralski process ( Figure 4 a). Monocrystalline material is widely used due to its high efficiency compared to multicrystalline material. Key technological challenges associated with monocrystalline silicon include stringent requirements for material purity, high material consumption during cell production, cell manufacturing processes, and limited module sizes composed of these cells [ 19 ].

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A picture showing ( a ) the Czochralski process for monocrystalline blocks and ( b ) the process of directional solidification for multicrystalline blocks [ 21 ].

Multicrystalline silicon blocks are produced through melting high-purity silicon and crystallizing it in a big crucible by directional solidification process ( Figure 4 b). There is no reference crystal orientation in this process, as in the Czochralski process, and therefore, silicon material with different orientations is produced. The most commonly used base material for solar cells are p-type Si substrates doped with boron. The n-type silicon substrates are also used for the fabrication of high-efficiency solar cells, but they present additional technical challenges, such as achieving uniform doping along the silicon block in comparison to p-type substrates [ 20 ].

In the production of crystalline solar cells, six or more steps need to be carried out sequentially. These typically include surface texturing, doping, diffusion, oxide removal, anti-reflective coating, metallization, and firing. At the end of the process, the cell efficiency and other parameters are measured (under standard test conditions). The efficiency of photovoltaic cells is determined by the material quality that is used in their manufacture [ 21 ].

The theoretical efficiency threshold for first-generation PV cells appears to have been estimated at 29.4%, and a sufficiently close value was reached as early as two decades ago. At the laboratory scale, reaching 25% efficiency was recorded as early as 1999, and since then, very minimal improvements in efficiency values have been achieved. Since the appearance of crystalline silicon photovoltaic cells, their efficiency has increased by 20.1%, from 6% when they were first discovered to the current record of 26.1% efficiency. There are factors that limit cell efficiency, such as volume defects. Breakthroughs in the production of these cells include the introduction of an aluminum back surface field (Al-BSF) to reduce the recombination rate on the back surface, or the development of Passivated Emitter and Rear Cell (PERC) technology to further reduce the recombination rate on the back surface [ 22 ].

2.1.1. Al-BSF Photovoltaic Cells

Silicon solar cells with distributed p-n junctions were invented as early as the 1950s, soon after the first semiconductor diodes. Originally, boron diffusion in arsenic-doped wafers was used to form p-n junctions, but now, the industry standard is phosphor diffusion in boron-doped wafers. After the transition in the 1960s from n-type wafers to p-type wafers, the implementation of an aluminum back-surface field (Al-BSF) by fusing the back contact to the substrate made it possible to reduce recombination on the back side ( Figure 5 ). This fairly simple contact screen printing design held a dominant position, with 70–90% of the market share for the past several decades [ 23 ].

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Silicon solar cell structure: Al-BSF [ 1 ].

Standard aluminum back surface field (Al-BSF) technology is one of the most widely used solar cell technologies due to its relatively simple manufacturing process. It is based on depositing Al entirely on the full rear-side (RS) in a screen-printing process and forming a p+ BSF, which helps repel electrons from the rear-side of the p-type substrate and improves the cell performance. The process flow of Al-BSF solar cell fabrication is shown in Figure 6 . Standard commercial solar cell design consists of a front side with a grid and a rear-side with full area contacts [ 24 ].

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Al-BSF solar cell manufacturing process [ 21 ].

2.1.2. PERC Photovoltaic Cells

The efficiency of the industrial Al-BSF cell, however, reached about 20% around 2013. It has therefore become attractive to replace the fully contacted Al-BSF cell with a PERC (Passivated Emitter and Rear Cell) structure with local back contacts to achieve enhanced electrical and optical properties ( Figure 7 ). The passivated emitter and rear contact (PERC) solar cell improves the Al-BSF architecture by the addition of a passivation layer on the rear side to improve passivation and internal reflection. Aluminum oxide has been found to be a suitable material for rear side passivation [ 25 ].

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Silicon solar cell structure: PERC [ 1 ].

The capability of this cell structure was demonstrated as early as the 1980s, although it was limited to laboratory processing because of its high cost relative to the yield gain. Moving the PERC technology into mass industrial production in theory involved a comparatively small industry threshold, as only two steps needed to be added to the Al-BSF process, i.e., passivation of the back surface and precise calibration of local back contacts. Nevertheless, decades passed before a profitable PERC process could be developed. A number of reasons led to the implementation of PERC in low-cost, high-volume production, and the increase in productivity to levels ranging from 22% to 23.4% [ 26 ]:

  • Introduction of aluminum oxide back surface passivation by plasma-enhanced chemical vapor deposition (PECVD) and formation of local back surface field (BSF) by laser ablation of back passivation layer and Al alloy;
  • Introduction of a selective emitter process in low-cost manufacturing, a “back-etching” process, or through a laser doping process;
  • Reducing the width of front metallization fingers from about 100 μm to less than 30 μm in high-volume production while reducing contact resistance for lightly phosphorus-doped silicon;
  • Adding a low-cost hydrogenation step at the end of the cell formation process to passivate volume defects and inactivate boron–oxygen complexes responsible for light-induced degradation (LID); and
  • Reappearance of monocrystalline silicon wafers as a result of cost reduction in silicon ingot production by the Czochralski method and the introduction of diamond wire cutting [ 27 ].

2.1.3. SHJ-Type Photovoltaic Cells

In parallel with PERC cells, other high-performance cell designs such as interdigitated back contact (IBC) solar cells and heterojunction solar cells (SHJ) have been introduced to mass production. Silicon heterojunction solar cells (SHJ), otherwise referred to as HIT cells, use passivating contacts based on a stack of layers of intrinsic and doped amorphous silicon ( Figure 8 ). Among the major technological challenges associated with this promising cell structure is that once the amorphous silicon layer is deposited, processes above 200 °C cannot be used. This rules out the well-known burned-in screen-printed metal contacts, and thus demands alternative methods using low-temperature pastes or galvanic contacts [ 28 ].

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Silicon solar cell structures: heterojunction (SHJ) in rear junction configuration [ 1 ].

There are currently intensive efforts to develop high-capacity production lines that could be competitive with present production standard lines. For SHJ technology to become widespread, there will be a need to overcome the challenges of increased cost of cell manufacturing tools, reducing the use of silver or replacing it with copper by developing Cu electroplating technology, as well as reducing the use of indium in the transparent conductive oxide (TCO) layer [ 29 ].

Moreover, as shown in Figure 9 , the HIT solar cell has a symmetric structure, which has two advantages. One is that the cell can be used in what is known as a bifacial module, which can generate more electricity than a regular module, and the other is that the structure is less stressed, which is important when processing thinner wafers [ 30 ].

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Structure of an HIT solar cell [ 30 ].

2.1.4. Photovoltaic Cells Based on Single III-V Junctions

GaAs-based single III-V junctions are reviewed at the end of this section. The III-V materials give the greatest photovoltaic conversion efficiency, achieving 29.1% with a GaAs single junction under single sunlight and 47.1% for a six-junction device under concentrated sunlight. These devices are also thinner (absorption layers typically being 2 to 5 µm thick) and thus could be fabricated as lightweight, flexible devices capable of being placed on curved surfaces. The III-V devices have high stability and have a history of high performance for challenging applications such as space [ 31 ].

The dominant III-V layer deposition process, metal–organic vapor phase epitaxy (MOVPE), holds the responsibility behind practically every performance record for III-V devices. Yet, historically, this process has been considered as a costly growth technique because of the high cost of precursors, the comparatively low usage of these precursors, and batch growth cycles that require many hours to be completed. Latest studies have significantly improved the growth rate and demonstrated much greater use of precursor chemicals using both MOVPE and hydrogen vapor phase epitaxy (HVPE) techniques, with HVPE also solving the precursor cost problem. Finishing currently includes a great number of labor-intensive, high-priced, and comparatively inefficient process steps, involving photolithography, manual application of spin coating, contact alignment, and metal evaporation and lifting [ 32 ].

2.2. Second Generation of Photovoltaic Cells

The thin film photovoltaic cells based on CdTe, gallium selenide, and copper (CIGS) or amorphous silicon have been designed to be a lower-cost replacement for crystalline silicon cells. They offer improved mechanical properties that are ideal for flexible applications, but this comes with the risk of reduced efficiency. Whereas the first generation of solar cells was an example of microelectronics, the evolution of thin films required new methods of growing and opened the sector up to other areas, including electrochemistry [ 33 ].

The second-generation photovoltaic cell comparison [ 18 ]:

  • Solar cells based on amorphous silicon (a-si)

Efficiency : 5 ÷ 12%; Band gap : ~1.7 eV; Life span : 15 years; Advantages : Less expensive, available in large quantities, non-toxic, high absorption coefficient; Restrictions : Lower efficiency, difficulty in selecting dopant materials, poor minority carrier lifetime.

  • Solar cells based on cadium telluride/cadium sulfide (CdTe/CdS)

Efficiency : 15 ÷ 16%; Band gap : ~1.45 eV; Life span : 20 years; Advantages : High absorption rate, less material required for production; Restrictions : Lower efficiency, Cd being extremely toxic, Te being limited, more temperature-sensitive.

  • Solar cells based on copper indium gallium selenide (CIGS)

Efficiency : 20%; Band gap : ~1.7 eV; Life span : 12 years; Advantages : Less material required for production; Restrictions : Very high-priced, not stable, more temperature-sensitive, highly unreliable [ 18 ].

2.2.1. CIGS Photovoltaic Cells

A key aspect that needed improvement was reducing the high dependence on semiconductor materials. This was the driving force that led to the emergence of the second generation of thin film photovoltaic cells, which include CIGS. In terms of efficiency, the record value for CIGS is 23.4%, which is comparable to the best silicon cell efficiencies. It should be noted, however, that the efficiency of the research cells does not directly translate to industrially achievable efficiency due to the nature of large-scale processing. Nevertheless, module efficiencies above 20% are already a reality. There has been a significant increase in the efficiency of CIGS cells in recent years and further increases are expected, for example, as a result of further research into alkaline treatment after deposition [ 34 ].

Group I-III-VI semiconducting chalcopyrite alloys (Ag,Cu)(In,Ga)(S,Se) 2 , commonly known as CIGS, are particularly favorable absorber materials for solar cells. They have direct band gaps ranging from ~1 to 2.6 eV, high absorption coefficients, and favorable internal defect parameters that allow high minority carrier lifetimes, and solar cells made from them are inherently stable in operation. The first recorded yield was 12% in a monocrystalline device in the mid-1970s. Subsequently, CIGS thin film absorbers, processing, and contacts were greatly improved, resulting in thin film cells with a small area and an efficiency of 23.4%. Current record module efficiencies are 17.6% on glass and 18.6% on flexible steel [ 35 ].

CIGS solar cells have been developed in a standard substrate configuration; however, deposition of CIGS at comparatively low temperatures on metal or polymer substrates to form flexible solar products is also possible. CIGS thin films are mainly being deposited by co-evaporation/devaporation or sputtering, and to a minor extent by electrochemical deposition as well as ion beam-assisted deposition. Since these are quaternary compounds, it is critical to control the stoichiometry of the thin film during fabrication. Work is also underway to produce fully or partially solution-deposited CIGS solar cells, and some predict that they could be the ultimate path to ultra-thin, coiled, and flexible PV modules [ 36 ].

The steps to improve the efficiency of CIGS cells may be described in the following way: (1) evaporation of CIS compound; (2) reactive elemental bilayer deposition; (3) selenization of sputtered metal precursors; (4) chemical bath deposition of CdS with ZnO:Al as emitter; (5) gallium alloying; (6) sodium alkali incorporation; (7) three-step co-deposition; (8) post-deposition treatment involving heavy alkali ion exchange; and (9) sulfurization after selenization (SAS). Progress is far from linear, with the complete potential for the optimization of the complex interactions between those techniques, along with others under development (e.g., silver alloys), yet to be achieved. A large number of scientists who specialize in CIGS think that efficiencies of 25% can be reached [ 37 ].

CIGS is a versatile material that can be produced by many processes and used in a variety of forms. There are currently four main categories of depositing methods used to fabricate CIGS films: (1) metal precursor deposition followed by sulfo-selenization; (2) reactive co-deposition; (3) electrodeposition; and (4) solution processing. All recent world records and the greatest commercial successes have been achieved by two-step sulfo-selenization of metal precursors or reactive co-deposition. CIGS can be deposited on a variety of substrates, including glass, metal films, and polymers. Glass is suitable for making rigid modules, while metal and polymer films allow applications that require lighter or flexible modules. With the evolution of global energy markets toward an appreciation of greenhouse gas reduction and circular economy aspects, the comparatively benign environmental impact of CIGS (especially without CdS) in comparison to different photovoltaic technologies is becoming the next competitive advantage [ 38 ].

Photovoltaic cells based on CIGS technology are composed of a pile of thin films deposited on a glass substrate by magnetron sputtering: a bottom molybdenum (Mo) electrode, a CIGS absorbing layer, a CdS buffer layer, and a zinc-doped oxide (ZnO:Al) top electrode. The co-evaporation and CdS buffer layer deposit the CIGS active layer by means of a chemical bath in a regular procedure ( Figure 10 ) [ 38 ].

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Demonstration of the CIGS-based standard solar cell stack [ 38 ].

2.2.2. CdTe Photovoltaic Cells

Second-generation photovoltaic cells also include CdTe-based solar cells. An interesting property of CdTe is the reduction in cell size—due to its high spectral efficiency, the absorber thickness can be reduced to about 1 μm without much loss in efficiency, although further work is needed ( Figure 11 ). Super-thin cells are particularly attractive for flexible applications, particularly in building-integrated photovoltaics (BIPV) due to their lighter weight, and transparent photovoltaic panels with CdTe can be developed due to the choice of transparent coating. Their transparency varies from about 10% to 50%, with the disadvantage that an increase in transparency necessarily decreases efficiency. Still, the transparent panels could replace window panels in buildings, not only generating electricity that could be used to power itself, but also contributing to noise reduction and thermal insulation, since most panels are encased in double glass [ 39 ].

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Schematic of a CdTe solar cell [ 1 ].

The technology of CdTe solar cells has developed considerably with the passage of time. In the 1980s, the efficiency of certified cells reached 10%, and in the 1990s, the efficiency was above 15% with the use of a glass/SnO 2 /CdS/CdTe layer structure and annealing in a CdCl 2 environment, and subsequent Cu diffusion. By the 2000s, efficiency of the cells hit 16.7% using sputtered Cd 2 SnO 4 and Zn 2 SnO 4 as transparent conductive oxide (TCO) layers. Over the past decade, new cell efficiency records have reached 22.1%. CdTe technology is increasingly used in rooftop systems and building-integrated photovoltaics [ 40 ].

In 2001, NREL produced a cell with an efficiency of 16.5%, which remained the benchmark for about 10 years. The record efficiency has been improved several times in the past 2 years by First Solar and GE Global Research. Currently, CdTe thin films account for less than 10% of the global PV market, with capacity expected to increase. Most of the commercial CdTe cells are manufactured by First Solar, which has achieved record cell efficiencies of 22.1% and average commercial module efficiencies of 17.5–18% [ 41 ].

The history of research and development and production of CdTe-based PV cells begins several decades beyond the first studies conducted by Bell Labs (Murray Hill, NJ, USA) in the 1950s on Si crystalline cells. The leading companies have been working on the commercialization of the underlying technology: Matsushita (Kadoma, Osaka, Japan), BP Solar (Madrid, Spain), Solar Cells Inc.—predecessor to First Solar (Tempe, AZ, USA), Abound Solar (Loveland, CO, USA) and GE PrimeStar (Denver, CO, USA). The top manufacturer of thin film CdTe PV is currently First Solar Solar (Tempe, AZ, USA), having fabricated 25 GW of PV modules since 2002 [ 42 ].

A range of comparatively easy and inexpensive approaches have been used to produce solar cells with 10–16% efficiency. Examples of several promising cheap deposition techniques include (1) close-space sublimation, (2) spray deposition, (3) electrodeposition, (4) screen printing, and (5) sputtering [ 43 ].

Recently, a record efficiency of 16% was reported in a CdS (0.4 μm)/CdTe (3.5 μm) thin film solar cell in which CdS and CdTe layers are deposited using metal–organic CVD (MOCVD) and CSS deposition techniques, respectively. Most of the high-performance solar cells use a device configuration of the superstrate type, where CdTe is deposited on a window layer of CdS. Typically, the structure of the device is composed of glass/CdS/CdTe/Cu-C/Ag. Most of the time, post-deposition heat treatment of the CdTe layer in the presence of CdCl 2 is necessary to optimize device performance [ 44 ].

The recent increase in efficiency is due partly to almost maximum photocurrent by optimizing the optical properties of the cell, deleting parasitically absorbing CdS and introducing CdSe x Te 1−x with a lower band gap. CdSe x Te 1-x extends the bandwidth of the absorber from ~1.4 to 1.5 eV and increases the carrier lifetime, thus improving photocurrent collection with no proportional loss of photocurrent. The use of ZnTe in the rear contact also improves the contact ohmicity significantly, and thus the efficiency [ 45 ].

2.2.3. Kesterite Photovoltaic Cells

In recent years, kesterite thin film materials have attracted more interest than CdTe and CIGS chalcogenide materials. Cu 2 ZnSnS x Se 4−x (CZTSSe) thin film photovoltaic material is attracting worldwide attention for its exceptional efficiency and composition derived from the Earth. A lot of research is being conducted on material engineering or designing new architecture to achieve high-performance CZTSSe thin film solar cells. Until recently, the most advanced thin film CZTSSe solar cells have been limited to 11.1% power conversion efficiency (PCE), with these efficiency levels reached using the hydrazine suspension method. Further vacuum and non-vacuum deposition techniques also proved effective in producing CZTSSe solar cells that had a PCE above 8%. Yet still, even record equipment with a PCE of 11% is significantly below the physical limit, generally referred to as the Shockley–Queisser (SQ) limit, which is around 31% efficiency under the Earth’s conditions [ 46 ].

A hydrazine-based pure solution method is used to prepare CZTSSe layers, and a Cu-poor and Zn-rich stoichiometry is adopted in the starting solution (Cu/(Zn + Sn) = 0.8 and Zn/Sn = 1.1). Multiple layers of components are spin-coated onto Mo-coated soda-lime glass and annealed at temperatures above 500 °C. Regarding the fabrication of devices, CZTSSe layers are deposited on Mo-coated glass substrates, then 25 nm CdS is deposited in a standard chemical bath and sputtered with 10 nm ZnO/50 nm ITO. A 2 μm thick Ni/Al top metal contact and 110 nm MgF 2 should be deposited on top of the devices by electron beam evaporation. The area of the device should be determined by mechanical scribing [ 47 ].

2.2.4. Photovoltaic Cells Based on Amorphous Silicon

The last type of cells classified as second-generation are devices that use amorphous silicon. Amorphous silicon (a-Si) solar cells are by far the most common thin film technology, whose efficiency is between 5% and 7%, rising to 8–10% for double and triple junction structures. Some varieties of amorphous silicon (a-Si) are amorphous silicon carbide (a-SiC), amorphous germanium silicon (a-SiGe), microcrystalline silicon (μ-Si), and amorphous silicon nitride (a-SiN). Hydrogen is required to dope the material, leading to hydrogenated amorphous silicon (a-Si:H). The gas phase deposition technique is typically used to form a-Si photovoltaic cells with metal or gas as the substrate material [ 48 ].

A typical manufacturing process for a-Si:H cells is the roll-to-roll process. First, a cylindrical sheet, usually stainless steel, is rolled out to be used as a deposition surface. The sheet is washed, cut to the desired size, and coated with an insulating layer. Next, a-Si:H is applied to the reflector, after which a transparent conductive oxide (TCO) is deposited on the silicon layer. Finally, laser cuts are made to join the different layers and the module is closed [ 49 ].

Amorphous silicon is usually deposited by plasma-enhanced vapor phase deposition (PECVD) at comparatively low substrate temperatures of 150–300 °C. A 300 nm thick a-Si:H layer is capable of absorbing about 90% of photons above the passband in a single pass, allowing the fabrication of lighter and more flexible solar cells [ 2 ].

Figure 12 shows the step-by-step fabrication process of an a-Si-based photovoltaic cell. Photovoltaic cells based on thin films are cheaper, thinner, and more flexible compared to first generation photovoltaic cells. The thickness of the light absorbing layer, which was 200–300 µm in first-generation photovoltaic cells, is 10 µm in second-generation cells. Semiconductor materials ranging from “micromorphic and amorphous silicon” to quaternary or binary semiconductors such as “cadmium telluride (CdTe) and copper indium gallium selenide (CIGS)” are used in thin films of photovoltaic cells [ 50 ].

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Manufacturing process of a-Si-based solar PV cell [ 2 ].

2.3. Third Generation of Photovoltaic Cells

The third generation of solar cells (including tandem, perovskite, dye-sensitized, organic, and emerging concepts) represent a wide range of approaches, from inexpensive low-efficiency systems (dye-sensitized, organic solar cells) to expensive high-efficiency systems (III-V multi-junction cells) for applications that range from building integration to space applications. Third-generation photovoltaic cells are sometimes referred to as “emerging concepts” because of their poor market penetration, even though some of these have been studied for more than 25 years [ 51 ].

The latest trends in silicon photovoltaic cell development are methods involving the generation of additional levels of energy in the semiconductor’s band structure. The most advanced studies of manufacturing technology and efficiency improvements are now concentrated on third-generation solar cells.

One of the current methods to increase the efficiency of PV cells is the introduction of additional energy levels in the semiconductor’s band gap (IBSC and IPV cells) and the increasing use of ion implantation in the manufacturing process. Other innovative third-generation cells that are lesser-known commercial “emerging” technologies include [ 52 ]:

  • Organic materials (OSC) photovoltaic cells;
  • Perovskites (PSC) photovoltaic cells;
  • Dye-sensitized (DSSC) photovoltaic cells;
  • Quantum dots (QD) photovoltaic cells; and
  • Multi-junction photovoltaic cells [ 52 ].

Third-generation photovoltaic cell comparison [ 18 ]:

  • Solar cells based on dye-sensitized photovoltaic cells

Efficiency : 5 ÷ 20%; Advantages : Lower cost, low light and wider angle operation, lower internal temperature operation, robustness, and extended lifetime; Restrictions : Problems with temperature stability, poisonous and volatile substances.

  • Solar cells based on quantum dots

Efficiency : 11 ÷ 17%; Advantages : Low production cost, low energy consumption; Restrictions : High toxicity in nature, degradation.

  • Solar cells based on organic and polymeric photovoltaic cells

Efficiency : 9 ÷ 11%; Advantages : Low processing cost, lighter weight, flexibility, thermal stability; Restrictions : Low efficiency.

  • Solar cells based on perovskite

Efficiency : 21%; Advantages : Low-cost and simplified structure, light weight, flexibility, high efficiency, low manufacturing cost; Restrictions : Unstable.

  • Multi-junction solar cells

Efficiency : 36% and higher; Advantages : High performance; Restrictions : Complex, expensive [ 18 ].

2.3.1. Organic and Polymeric Materials Photovoltaic Cells (OSC)

Organic solar cells (OSCs) are beneficial in applications related to solar energy since they have the potential to be used in a variety of prospects on the basis of the unique benefits of organic semiconductors, including their ability to be processed in solution, light weight, low cost, flexibility, semi-transparency, and applicability to large-scale roll-to-roll processing. Solution-processed organic solar cells (OSCs) that absorb near-infrared (NIR) radiation have been studied worldwide for their potential to be donor:acceptor bulk heterojunction (BHJ) compounds. In addition, NIR-absorbing OSCs have attracted attention as high-end equipment in next-generation optoelectronic devices, such as translucent solar cells and NIR photodetectors, because of their potential for industrial applications. With the introduction of non-fullerene acceptors (NFAs) that absorb light in the NIR range, the value of OSC is increasing, while organic donor materials capable of absorbing light in the NIR range have not yet been actively studied compared to acceptor materials that absorb light in the NIR range [ 53 ].

The most advanced BHJ structure by combining organic donor and acceptor materials showed tremendous hope for low-cost and lightweight organic solar cells. Over the past decade, enormous progress was made, with power conversion efficiencies reaching more than 14% for a single-junction device and more than 17% for a tandem device through the design of new NIR photoactive materials with low bandwidth. Compared to wide-band organic photovoltaic materials, low-band donor and non-fullerene acceptor materials with wide-range solar coverage extended to the NIR region typically exhibit more tightly superimposed electronic orbitals, easier delocalization of π electrons, higher dielectric constant, stronger dipole moment, and lower exciton binding energy. These properties make low-bandwidth photovoltaic materials play an important role in high-performance organic solar cells, including single-junction and tandem devices [ 54 ].

A clever strategy in active layer design could be summed up as optimizing the weight ratio of donor to acceptor materials, using ultra-low band gap materials as a third component to improve NIR light utilization efficiency, and adjusting the thickness of the active layer to achieve a compromise between photon collection and charge accumulation. Much effort has gone into optimizing the translucent top electrode: well-balanced conductivity and transmittance in the visible light range, increased reflectance in the NIR or ultraviolet (UV) light range, and better compatibility with active layers. In terms of device engineering, photon crystal, anti-reflection coating, optical microcavity, and dielectric/metal/dielectric (DMD) structures have been placed to realize selective transmission and reflection for simultaneous improvement of power conversion efficiency and average transmission of translucent OSC visible light [ 55 ].

2.3.2. Dye-Sensitized Photovoltaic Cells (DSSC)

Conjugated polymers and organic semiconductors have been successful in flat panel displays and LEDs, so they are considered advanced materials in the current generation of photovoltaic cells. A schematic representation of dye-sensitized organic photovoltaic cells (DSSCs) is shown in Figure 13 . Polymer/organic photovoltaic cells can also be divided into dye-sensitized organic photovoltaic cells (DSSCs), photoelectrochemical photovoltaic cells, and plastic (polymer) and organic photovoltaic devices (OPVDs), differing in mechanism of operation [ 56 ].

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Schematic representation of a DSSCs [ 2 ].

Dye-sensitized solar cells (DSSCs) represent one of the best nanotechnology materials for energy harvesting in photovoltaic technologies. It is a hybrid organic–inorganic structure where a highly porous, nanocrystalline layer of titanium dioxide (TiO 2 ) is used as a conductor of electrons in contact with an electrolyte solution also containing organic dyes that absorb light near the interfaces. A charge transfer occurs at the interface, resulting in the transport of holes in the electrolyte. The power conversion efficiency has been shown to be about 11%, and commercialization of dye-sensitized photovoltaic modules is underway. A novel feature in DSSC solar cells is the photosensitization of nanosized TiO 2 coatings in combination with optically active dyes, which increases their efficiency to more than 10% [ 57 ].

DSSCs hold promise as photovoltaic devices because of their simple fabrication, low material costs, and their benefits in transparence, color capability, and mechanical flexibility. The main challenges in commercializing DSSCs are poor photoelectric conversion efficiency and cell stability. The highest attainable theoretical energy conversion efficiency was estimated at 32% for DSSCs; however, the highest efficiency reported to date is only 13%. Intensive work is underway to understand the parameters governing the DSSC to improve its efficiency. Numerous attempts have been made to optimize the redox pair and absorbance of the dye, modify a wide band gap semiconductor as a working electrode, and develop a counter electrode (CE). In addition to increasing the efficiency of DSSC, the cost of materials is another major issue that needs to be solved in future work [ 58 ].

2.3.3. Perovskite Photovoltaic Cells

Perovskite solar cells (PSCs) are a revolutionary new photovoltaic cell concept that relies on metal halide perovskites (MHPs), e.g., methylammonium iodide as well as formamidine lead iodide (MAPbI 3 or FAPbI 3 , respectively). MHPs integrate a number of features favored in photovoltaic absorbers, including a direct band gap with a high absorption coefficient, long carrier lifetime and diffusion length, low defect density, and ease of tuning the composition and band gap. In the year 2009, MHP was first described as a sensitizer in a dye cell based on liquid electrolyte conducting holes. In 2012, MHP demonstrating ~10% efficiency of PSCs based on a solid-state hole conductor sparked an explosion of PSC studies. In about a decade of research, the efficiency of a single PSC junction increased to a certified level of 25.2% [ 59 ].

The development of PSCs has been heavily influenced by the improvement of material quality through a broad range of synthetic methods designed under the guidance of a fundamental understanding of MHP growth mechanisms. Comprehension of the complex and correlated processes of perovskite growth (e.g., nucleation, grain growth, as well as microstructure evolution) has aided in the development of a broad range of high-efficiency growth modes (for example, single-step growth, sequential growth, dissolution process, vapor process, post-deposition processing, non-stoichiometric growth, additive-assisted growth, and fine-tuning of structure dimensions). The latest efforts were concentrated on interface engineering, focusing on reducing open-circuit voltage losses and improving stability, particularly by introducing a two-dimensional perovskite surface layer. With progress in synthetic control, the perovskite composition is becoming simpler, mainly toward FAPbI 3 . This will undoubtedly contribute to the simplification of scale deposition methods and a basic understanding of the properties of these cells [ 60 ].

2.3.4. Quantum Dots Photovoltaic Cells

Solar cells made from these materials are called quantum dots (QDs) and are also known as nanocrystalline solar cells. They are fabricated by epitaxial growth on a substrate crystal. Quantum dots are surrounded by high potential barriers in a three-dimensional shape, and the electrons and electron holes in a quantum dot become discrete energy because they are confined in a small space ( Figure 14 ). Consequently, the ground state energy of electrons and electron holes in a quantum dot depends on the size of the quantum dot [ 61 ].

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( a ) A scheme of a solar cell based on quantum dots, ( b ) solar cell band diagram [ 64 ].

Nanocrystalline cells have relatively high absorption coefficients. Four consecutive processes occur in a solar cell: (1) light absorption and exciton formation, (2) exciton diffusion, (3) charge separation, and (4) charge transport. Due to the poor mobility and short lifetime of excitons in conducting polymers, organic compounds are characterized by small exciton diffusion lengths (10–20 nm). In other words, excitons that form far from the electrode or carrier transport layer recombine and the conversion efficiency drops [ 62 ].

The development of thin film solar cells with metal halide perovskites has led to intensive attention to the corresponding nanocrystals (NCs) or quantum dots (QDs). Today, the record efficiency of QD solar cells was improved to 16.6% using mixed colloidal QDs with perovskites. The universality of these new nanomaterials regarding ease of fabrication and the ability to tune the band gap and control the surface chemistry allows a variety of possibilities for photovoltaics, such as single-junction, elastic, translucent, controlled cells with heterostructures and multi-junction tandem solar cells which would push the field even further. However, a narrower size distribution has the potential to enhance the performance of QD solar cells through more ways than one. Firstly, electron transport might be better in smaller QDs, as larger QDs function as a band tail or shallow trap that makes transport more difficult. Secondly, the open-circuit voltage (V OC ) of QD solar cells could be limited by the smallest band gap (largest size) QD near the contacts. Enhancing the homogeneity and uniformity of QD size would also improve PV performance by the minimization of such losses. Although controlled experiments such as these have not yet been reported, it is possible that more controlled synthesis might provide benefits to QD cells [ 63 ].

2.3.5. Multi-Junction Photovoltaic Cells

Multi-junction (MJ) solar cells consist of plural p-n junctions fabricated from various semiconductor materials, with each junction producing an electric current in response to light of a different wavelength, thereby improving the conversion of incident sunlight into electricity and the efficiency of the device. The concept to use various materials with different band gaps has been suggested to utilize the maximum possible number of photons and is known as a tandem solar cell. An entire cell could be fabricated from the same or different materials, giving a broad spectrum of possible designs [ 65 ].

Usually, the cells are integrated monolithically and connected in series through a tunnel junction, and current matching between cells is obtained through adjusting each cell’s band gap and thickness. The theoretical feasibility of using multiple band gaps was examined and was found to be 44% for two band gaps, 49% for three band gaps, 54% for four band gaps, and 66% for an infinite number of gaps. Figure 15 illustrates a scheme of an InGaP/(In)GaAs/Ge triple solar cell and presents crucial technologies to enhance efficiency of conversion [ 66 ].

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Schematic illustration of a triple-junction cell and approaches for improving efficiency of the cell [ 65 ].

Grid-matched InGaP/(In)GaAs/Ge triple solar cells have been widely used in space photovoltaics and have achieved the highest true efficiency of over 36%. Heavy radiation bombardment of various energetic particles in the space environment inevitably damages solar cells and causes the formation of additional non-radiative recombination centers, which reduces the diffusion length of minority carriers and leads to a reduction in solar cell efficiency. The sub-cells in multi-junction solar cells are connected in series; the sub-cell with the greatest radiation degradation degrades the efficiency of the multi-junction solar cell. To improve the radiation resistance of (In)GaAs sub-cells, measures such as reducing the dopant concentration, decreasing the thickness of the base region, etc., can be used [ 66 ].

2.3.6. Photovoltaic Cells with Additional Intermediate Band

The National Renewable Energy Laboratory (NREL) estimates that multi-junction and IBSC photovoltaic cells have the highest efficiency under experimental conditions (47.1%). The main feature of these cells is precisely the additional intermediate band in the band gap of silicon. Currently, two types of these cells are specified in the world literature: IBSC (Intermediate Band Solar Cells) and IPV (Impurity Photovoltaic Effect) [ 67 ].

Impurity Photovoltaic Effect (IPV) is one of the solutions used to increase the infrared response of PV cells and thus increase the solar-to-electric energy conversion efficiency. The idea of the IPV effect is based on the introduction of deep radiation defects in the structure of the semiconductor crystal structure. These defects ensure a multi-step absorption mechanism for photons with energies below the band gap width. The addition of IPV dopants into silicon solar cell structure, under certain conditions, increases the spectral response, short circuit current density, and conversion efficiency [ 68 ].

A major direction of study with great potential for development is Intermediate Band Solar Cells (IBSCs). They represent a third-generation solar cell concept and involve not only silicon, but also other materials. The idea behind the intermediate band gap solar cell (IBSC) concept is to absorb photons with an energy corresponding to the sub-band width in the cell structure. These photons are absorbed by a semiconductor-like material that, in addition to the conduction and valence bands, has an intermediate band (IB) in the conventional semiconductor’s band gap ( Figure 16 ). In IBSCs, the silicon layers are implanted with very high doses of metal ions to create an additional energy level [ 69 ].

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Energy band diagram of an intermediate band solar cell (IBSC) [ 69 ].

Based on the research conducted on the effect of defects introduced into the silicon structure, a model was developed according to which introducing selected deep defects into the charge carrier capture region results in improved PV cell efficiency. Of particular interest are defects that facilitate the transport of majority carriers and defects that counteract the accumulation of minority carriers. This contributes significantly to reducing the recombination process at the charge carrier capture site. Finally, by introducing defects into the structure of the silicon underlying the solar cell, we combine effective surface passivation with simultaneous reduction in optical losses [ 70 ].

The introduction of intermediate bands in semiconductors, using ion implantation, can be executed using two methods: by introducing dopants of very high concentration into the semiconductor substrate, or by implanting the silicon layer with high-dose metal ions. The increasing use of ion implantation in the photovoltaic cell manufacturing process has the potential to reduce the cost of deployment and increase the cost-effectiveness of silicon cells by increasing their efficiency. The use of ion implantation technology provides increased precision of silicon layer doping and generation of additional levels of energy in the band gap, as well as shortening the individual stages of cell fabrication, which ultimately translates into improved quality and lower production costs [ 71 ].

Lately, the technique of ion implantation is gaining popularity in the solar industry, gradually displacing the diffusion technique that has been used for many years. As can be seen in Figure 17 , cell performance is expected to continue to improve as the technology evolves toward higher efficiencies. In addition to local and reference doping, the major benefits of this technology involve high precision control of the amount and distribution of dopant doses, which results in high uniformity, repeatability, and increased efficiency (above 19%), with a significantly narrower distribution of cell performance [ 72 ].

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Stabilized cell efficiency trend curves [ 72 ].

In the method of ion implantation, chosen ions with the required impurity are inserted into the semiconductor by accelerating the impurity ions to a high energy level and implanting the ions into the semiconductor. The energy given to the impurity ions defines the depth of ion implantation. Contrary to the diffusion technology (where the impurity ion dose is introduced only at the surface), in the ion implantation technique, a controllable dose of impurity ions can be placed deeply into the semiconductor [ 73 ].

2.4. Fourth Generation of Photovoltaic Cells

Fourth-generation photovoltaic cells are also known as hybrid inorganic cells because they combine the low cost and flexibility of polymer thin films, with the stability of organic nanostructures such as metal nanoparticles and metal oxides, carbon nanotubes, graphene, and their derivatives. These devices, often referred to as “nanophotovoltaics”, could become the promising future of photovoltaics [ 74 ].

Graphene-Based Photovoltaic Cells

By using thin polymer layers and metal nanoparticles, as well as various metal oxides, carbon nanotubes, graphene, and their derivatives, the fourth generation provides excellent affordability and flexibility. Particular emphasis was placed on graphene because it is considered a nanomaterial of the future. Due to their unique properties, such as high carrier mobility, low resistivity and transmittance, and 2D lattice packing, graphene-based materials are being considered for use in PV devices instead of existing conventional materials. However, to achieve adequate device performance, the key to its practical applications is the synthesis of graphene materials with appropriate structure and properties [ 75 ].

Since the properties of graphene are fundamentally related to its fabrication process, a judicious choice of methods is essential for targeted applications. In particular, highly conductive graphene is suitable for use in flexible photovoltaic devices, and its high compatibility with metal oxides, metallic compounds, and conductive polymers makes it suitable for use as a selective charge-taking element and electrode interlayer material [ 76 ].

In the past two decades, graphene has been combined with the concept of photovoltaic material and is showing a significant role as a transparent electrode, hole/electron transport material, and interfacial buffer layer in solar cell devices. We can distinguish several types of graphene-based solar cells, including organic bulk heterojunction (BHJ) cells, dye-sensitized cells, and perovskite cells. The energy conversion efficiency exceeded 20.3% for graphene-based perovskite solar cells and reached 10% for BHJ organic solar cells. In addition to its function of extracting and transporting charge to the electrodes, graphene plays another unique role—it protects the device from environmental degradation through its packed 2D lattice structure and ensures the long-term environmental stability of photovoltaic devices [ 77 ].

Semi-metallic graphene having a zero band gap creates Schottky junction solar cells with silicon semiconductors. Even though graphene was discovered for the first time in 2004, the first graphene–silicon solar cell was not characterized as an n-silicon cell until 2010. Figure 18 schematically shows a graphene–silicon solar cell with a Schottky junction. Graphene sheets (GS), cultured by chemical vapor deposition (CVD) on nickel films, were wet deposited on pre-patterned Si/SiO 2 substrates with an effective area of 0.1–0.5 cm 2 . The graphene sheet forms a coating on the exposed n-Si substrate, creating a Schottky junction. The graphene sheet was contacted using Au electrodes [ 78 ].

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Graphene–silicon Schottky junction solar cell. ( a ) Cross-sectional view, ( b ) schematic illustration of the device configuration [ 75 ].

Graphene synthesis uses mainly two methodologies, which are the bottom-up and top-down methods. In the top-down approach, graphite is the starting material, and the goal is to intercalate and exfoliate it into graphene sheets by solid, liquid, or electrochemical exfoliation. Another approach under this categorization is the exfoliation of graphite oxide into graphene oxide (GO), after which chemical or thermal reduction takes place. A bottom-up approach is to produce graphene from molecular precursors by chemical vapor deposition (CVD) or epitaxial growth. The structure, morphology, and attributes of the resulting graphene, including the layer numbers, level of defects, electrical and thermal conductivity, solubility, and hydrophilicity or hydrophobicity, are dependent on the manufacturing process [ 78 , 79 ].

Graphene can absorb 2.3% of incident white light even though it is only one atom thick. Incorporating graphene into a silicon solar cell is a promising platform since graphene has a strong interaction with light, fulfilling both the optical (high transmittance) and electrical (low layer resistance) requirements of a typical transparent conductive electrode. It is important to note that both the layer resistance and the transmittance of graphene change with the number of layers. As the layer resistance decreases as the number of graphene layers increases, the optical transparency decreases as well [ 80 ].

For PV technology, graphene offers a lot more because of its flexibility, environmental stability, low electrical resistivity, and photocatalytic features, while having to be carefully and deliberately designed for the targeted applications and specific requirements [ 78 , 80 ].

One problem for graphene application is the absence of a simpler, more reliable way to deposit a well-ordered monolayer with low-cost flakes on target substrates having various surface properties. The other problem is the adhesion of the deposited graphene thin film, a subject that has not yet been studied properly. Large-area continuous graphene layers with high optical transparency and electrical conductivity may be fabricated by CVD. As an anode in organic photovoltaic devices, graphene holds great promise as a replacement for indium tin oxide (ITO) because of its inherently low-cost manufacturing process and excellent conductivity and transparency properties [ 81 ].

Graphene’s major disadvantage is its poor hydrophilicity, which negatively affects the design of devices processed in solution, but that fact may be overcome through modifying the surface by non-covalent chemical functionalization. Given graphene’s mechanical strength and flexibility, as well as its excellent conductivity properties, it can be anticipated that new applications in plastic electronics and optoelectronics will soon emerge involving this new class of CVD graphene materials. The discovery paves the way for low-cost graphene layers to replace ITO in photovoltaic and electroluminescent devices [ 82 ].

3. Prospects and Research Directions

Since the beginning of photovoltaic cells, crystalline silicon-based photovoltaic technology has played a dominant role in the market, with crystalline PV modules accounting for about 90% of the market share in 2020. In recent years, there has been a rapid development of thin film solar cells (such as cadmium telluride (CdTe) and indium–gallium selenium compounds (CIGS) cells) and new solar cells (such as dye-sensitized solar cells (DSSCs), perovskite solar cells (PSCs), quantum dot solar cells (QDSCs), etc.) [ 83 ].

The growing interest in BIPV systems has contributed to the overall development of photovoltaic technology, which has led to lower costs, increasing the feasibility of investment. Most of the standard second-generation technologies show efficiencies of 20–25%, and while they are expensive, the cost of silicon cells has come down and it is the improvement of silicon technologies that is now one of the key research directions [ 84 ].

Graphene and its derivatives are a promising area of research as they are in the early stages of research and development. The goal of using carbon nanostructures is to produce energy-efficient products that combine transport, active, and electrode layers. Many researchers in contemporary graphene research are now focusing on new graphene derivatives and their novel applications in manufacturing devices [ 85 ].

Nevertheless, the technologies used for third- and fourth-generation cells are still in the prototyping stage. Production-scale prototypes have also been built and have been successful (10–17% efficiency). In contrast, third-generation multi-junction cells are already commercially available and have achieved exceptional conversion factors (from 40% to over 50%) that place this alternative as the best [ 85 ]. Considering the market trends of increasing use of intermediate energy levels in PV cell production, it makes perfect sense to conduct research in this direction, which is exactly what our research team is doing.

The practical realization of the idea of energy-efficient IBSC-type silicon solar cells with intermediate energy levels in the band gap of the semiconductor, produced by ion implantation, needs more studies directed at the search for the optimal implantation parameters, which is the energy, type, and dose of ions, adjusted to the substrate material properties, particularly the level and type of dopant [ 86 ].

It appears that implantation can also lead to a reduction in the optical losses present in the cell. Impurities and defects introduced into the silicon crystal lattice under the right conditions can create additional intermediate band gaps, which realistically contributes to the reduction in the energy gap width. As a result, some photons with energies lower than the band gap value cause the formation of additional electron–hole pairs. The existence of this additional energy band contributes to the increase in the value of the photoelectric current, which results from the absorption of photons not previously involved in the photovoltaic conversion process. The range of absorbed light radiation increases toward the infrared, and after absorbing a photon from this range, the electron goes first to the intermediate band and then to the conduction band [ 87 ].

Our long-standing studies on changing the electrical parameters of silicon through the use of neon ion implantation have resulted in the development of the authorial methodology for the generation and identification of additional levels of energy in the silicon band structure, improving the efficiency of photovoltaic cells made based on it [ 88 ].

The research has been directed at determining the effect of the degree and type of silicon defect in terms of the possibility of producing intermediate energy levels in the semiconductor’s band gap, thereby increasing the efficiency of solar cells by enabling a multi-step transition of electrons from the valence band to the intermediate band and then to the conduction band.

The object of our research is a method of producing intermediate energy levels in the band gap of n- and p-type silicon, with a specific resistivity ρ ranging from 0.25 Ω·cm to 10 Ω·cm, by generating deep radiation defects in the crystal structure of the semiconductor by implantation of Ne + neon ions. The research material is doped with elements such as boron, phosphorus, and antimony.

Neon ions were chosen because the ions primarily produce point defects, the deliberate introduction of which into the crystalline lattice of silicon in the process of implantation makes it possible to alter its fundamental electrical parameters, including energy gap width and resistivity. The parameters significantly affect internal losses in photovoltaic cells [ 89 ]. Experimental studies were conducted to provide details for determination of the optimal dose of implanted neon ions because of their ability to generate intermediate energy levels in the semiconductor band gap.

The Results of the Author’s Research

The silicon samples were implanted with neon ions of energy E = 100 keV and different doses D using a UNIMAS 79 ion implanter and then isochronically annealed at 598 K for 15 min in a resistance furnace. The electrical parameters of the silicon samples were tested using a Discovery DY600C climate chamber using the proprietary PV Cells Meter computer program and the Winkratos software. A GW Instek LCR-8110G Series LCR meter was used to measure capacitance and conductance values, while sample temperature values were measured using Fluke 289 and Lutron TM-917 multimeters ( Figure 19 ).

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Silicon samples laboratory stand. ( a ) Schematic diagram of the laboratory stand: 1—solar cell, 2—supporting construction, 3—temperature sensor, 4—pyranometer, 5—light source, V1—Fluke 289, V2—The LCR-8110G Series LCR meter, RC—shunt resistor, RL—adjustable load. ( b ) Special measuring holders inside the climate chamber to hold silicon samples. ( c ) Discovery DY600C climate chamber [ 90 ].

The resulting capacitance and conductance measurements allowed us to determine the position values of the additional energy levels in the band gap. Two methods were used for this purpose. The first is the Thermal Admittance Spectroscopy (TAS) method, by which it was possible to determine the e t ( T p ) rate that determines the thermal emission, followed by the Arrhenius curves. By using the Arrhenius equation, it was possible to determine the activation energies of the deep energy levels by approximating the experimental data with a linear function [ 86 ]. An example of the results obtained by the TAS method is shown in Figure 20 a.

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The Arrhenius law approximation ranges for silicon implanted with neon Ne + ions of energy E = 100 keV ( a ) P-type silicon doped with boron, ρ = 0.4 Ω·cm, D = 2.2 × 10 14 cm −2 , Δ E = 0.46 eV. ( b ) N-type silicon doped with phosphorus, ρ = 10 Ω·cm, D = 4.0 × 10 14 cm −2 , Δ E = 0.23 eV [ 86 , 87 ].

Another method of determining the activation energy is the approximation of selected parts of the course C p = f(1000/ T p ) with the function of the equation ln(y) = Ax + B, where C p is the unit capacitance of the tested sample, and T p is the temperature of the sample during the measurements performed at the frequency of the measuring signal f = 100 kHz. This in turn allowed the calculation of the conduction activation energy Δ E , which determines the depth of the additional intermediate energy level [ 87 ]. An example of the results obtained by the Arrhenius curve approximation method is shown in Figure 20 b.

On the basis of the conducted research, it was possible to identify radiation defects that create additional energy levels in the silicon band gap, with corresponding activation energies, where the results are shown in Table 1 . Our research proved that the implantation of Ne+ ions results in generating radiation defects in the crystal lattice of silicon as a photovoltaic cell base material and enables the generation of intermediate levels of energy in the band gap, improving the efficiency of photovoltaic cells made on its basis.

Determination of intermediate energy levels for boron and phosphorus doped silicon samples implanted with Ne + ions and energy E = 100 keV, isochronically annealed at 598 K [ 86 , 87 ].

SampleLabelResistivityDoseActivation Energy

Si+B = 0.4 Ω·cm = 4.0 × 10 cm Δ = 0.34 eV

Si+B = 0.4 Ω·cm = 2.2 × 10 cm Δ = 0.46 eV

Si+B = 0.4 Ω·cm = 4.0 × 10 cm Δ = 0.32 eV

Si+P = 10 Ω·cm = 4.0 × 10 cm Δ = 0.19 eV

Si+P = 10 Ω·cm = 4.0 × 10 cm Δ = 0.23 eV

4. Conclusions

Solar energy is one of the most demanding renewable sources of electricity. Electricity production using photovoltaic technology not only helps meet the growing demand for energy, but also contributes to mitigating global climate change by reducing dependence on fossil fuels. The level of competitiveness of innovative next-generation solar cells is increasing due to the efforts of researchers and scientists related to the development of new materials, particularly nanomaterials and nanotechnology.

It is noted that the solar cell market is dominated by monocrystalline silicon cells due to their high efficiency. About two decades ago, the efficiency of crystalline silicon photovoltaic cells reached the 25% threshold at the laboratory scale. Despite technological advances since then, peak efficiency has now increased very slightly to 26.6%. As the efficiency of crystalline silicon technology approaches the saturation curve, researchers around the world are exploring alternative materials and manufacturing processes to further increase this efficiency. Polycrystalline and amorphous thin film silicon cells are seen as a serious competitor to monocrystalline silicon cells. However, their disadvantage is their disordered nature which results in low efficiency.

In this paper is a comprehensive overview of various PV technologies that are currently available or will be available in the near future on a commercial scale. A comparative analysis in terms of efficiency and the technological processes used is presented. Over the past few decades, many new materials have emerged that provide an efficient source of power generation to meet future demands while being cost-effective. This paper is a comprehensive study covering the generations of photovoltaic cells and the properties that characterize these cells. Photovoltaic cell materials of different generations have been compared based on their fabrication methods, properties, and photoelectric conversion efficiency.

First-generation solar cells are conventional and based on silicon wafers. The second generation of solar cells involves thin film technologies. The third generation of solar cells includes new technologies, including solar cells made of organic materials, cells made of perovskites, dye-sensitized cells, quantum dot cells, or multi-junction cells. With advances in technology, the drawbacks of previous generations have been eliminated in fourth-generation graphene-based solar cells. The popularity of photovoltaics depends on three aspects—cost, raw material availability, and efficiency. Third-generation solar cells are the latest and most promising technology in photovoltaics. Research on these is still in progress. This review pays special attention to the new generation of solar cells: multi-junction cells and photovoltaic cells with an additional intermediate band.

Recent advances in multi-junction solar cells based on n-type silicon and functional nanomaterials such as graphene offer a promising alternative to low-cost, high-efficiency cells. Currently, multi-junction cells, which benefit from advances enabled by nanotechnology, are breaking efficiency records. They are still quite expensive and represent a complex system, but there are simpler alternatives that may eventually provide a path to the competitiveness of the highest efficiency devices. Another significant advance is being made in the generation of additional energy levels in the band structure of silicon. In both cases, more research evidence, policies, and technology are needed to make them accessible. Therefore, it remains crucial to develop silicon-based technologies. The use of these new solar cell architectures would provide a new direction toward achieving commercial goals. Multi-junction based solar cells and new photovoltaic cells with an additional intermediate energy level are expected to provide extremely high efficiency. The research in this case focuses on a low-cost manufacturing process. Therefore, commercialization of these cells requires further work and exploration.

Nanotechnology and newly developed multifunctional nanomaterials can help overcome current performance barriers and significantly improve solar energy generation and conversion through photovoltaic techniques. Many physical phenomena have been identified at the nanoscale that can improve solar energy generation and conversion. However, the challenges associated with these technologies continue to be an issue when they are incorporated into PV manufacturing. Thanks to initial successes in recent years, nanomaterials are one of the most promising energy technologies of the future and are expected to significantly reform the future energy market. Carbon nanoparticles and their allotropic forms, such as graphene, are expected to offer high efficiency compared to conventional silicon cells in the near future and thus contribute to new prospects for the solar energy market.

Funding Statement

This research was funded by the Lublin University of Technology, grant number FD-20/EE-2/708.

Author Contributions

P.W. proposed a study on photovoltaic cell generations and current research directions for their development and guided the work. J.P. conducted a literature review and wrote the paper. J.P. and P.W. described further prospects and research directions and outlined conclusions based on the collected literature. P.W. reviewed and edited the work. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Informed consent statement, data availability statement, conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Article Contents

Introduction, 1 installed capacity and application of solar energy worldwide, 2 the role of solar energy in sustainable development, 3 the perspective of solar energy, 4 conclusions, conflict of interest statement.

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Solar energy technology and its roles in sustainable development

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Ali O M Maka, Jamal M Alabid, Solar energy technology and its roles in sustainable development, Clean Energy , Volume 6, Issue 3, June 2022, Pages 476–483,

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Solar energy is environmentally friendly technology, a great energy supply and one of the most significant renewable and green energy sources. It plays a substantial role in achieving sustainable development energy solutions. Therefore, the massive amount of solar energy attainable daily makes it a very attractive resource for generating electricity. Both technologies, applications of concentrated solar power or solar photovoltaics, are always under continuous development to fulfil our energy needs. Hence, a large installed capacity of solar energy applications worldwide, in the same context, supports the energy sector and meets the employment market to gain sufficient development. This paper highlights solar energy applications and their role in sustainable development and considers renewable energy’s overall employment potential. Thus, it provides insights and analysis on solar energy sustainability, including environmental and economic development. Furthermore, it has identified the contributions of solar energy applications in sustainable development by providing energy needs, creating jobs opportunities and enhancing environmental protection. Finally, the perspective of solar energy technology is drawn up in the application of the energy sector and affords a vision of future development in this domain.


With reference to the recommendations of the UN, the Climate Change Conference, COP26, was held in Glasgow , UK, in 2021. They reached an agreement through the representatives of the 197 countries, where they concurred to move towards reducing dependency on coal and fossil-fuel sources. Furthermore, the conference stated ‘the various opportunities for governments to prioritize health and equity in the international climate movement and sustainable development agenda’. Also, one of the testaments is the necessity to ‘create energy systems that protect and improve climate and health’ [ 1 , 2 ].

The Paris Climate Accords is a worldwide agreement on climate change signed in 2015, which addressed the mitigation of climate change, adaptation and finance. Consequently, the representatives of 196 countries concurred to decrease their greenhouse gas emissions [ 3 ]. The Paris Agreement is essential for present and future generations to attain a more secure and stable environment. In essence, the Paris Agreement has been about safeguarding people from such an uncertain and progressively dangerous environment and ensuring everyone can have the right to live in a healthy, pollutant-free environment without the negative impacts of climate change [ 3 , 4 ].

In recent decades, there has been an increase in demand for cleaner energy resources. Based on that, decision-makers of all countries have drawn up plans that depend on renewable sources through a long-term strategy. Thus, such plans reduce the reliance of dependence on traditional energy sources and substitute traditional energy sources with alternative energy technology. As a result, the global community is starting to shift towards utilizing sustainable energy sources and reducing dependence on traditional fossil fuels as a source of energy [ 5 , 6 ].

In 2015, the UN adopted the sustainable development goals (SDGs) and recognized them as international legislation, which demands a global effort to end poverty, safeguard the environment and guarantee that by 2030, humanity lives in prosperity and peace. Consequently, progress needs to be balanced among economic, social and environmental sustainability models [ 7 ].

Many national and international regulations have been established to control the gas emissions and pollutants that impact the environment [ 8 ]. However, the negative effects of increased carbon in the atmosphere have grown in the last 10 years. Production and use of fossil fuels emit methane (CH 4 ), carbon dioxide (CO 2 ) and carbon monoxide (CO), which are the most significant contributors to environmental emissions on our planet. Additionally, coal and oil, including gasoline, coal, oil and methane, are commonly used in energy for transport or for generating electricity. Therefore, burning these fossil fuel s is deemed the largest emitter when used for electricity generation, transport, etc. However, these energy resources are considered depleted energy sources being consumed to an unsustainable degree [ 9–11 ].

Energy is an essential need for the existence and growth of human communities. Consequently, the need for energy has increased gradually as human civilization has progressed. Additionally, in the past few decades, the rapid rise of the world’s population and its reliance on technological developments have increased energy demands. Furthermore, green technology sources play an important role in sustainably providing energy supplies, especially in mitigating climate change [ 5 , 6 , 8 ].

Currently, fossil fuels remain dominant and will continue to be the primary source of large-scale energy for the foreseeable future; however, renewable energy should play a vital role in the future of global energy. The global energy system is undergoing a movement towards more sustainable sources of energy [ 12 , 13 ].

Power generation by fossil-fuel resources has peaked, whilst solar energy is predicted to be at the vanguard of energy generation in the near future. Moreover, it is predicted that by 2050, the generation of solar energy will have increased to 48% due to economic and industrial growth [ 13 , 14 ].

In recent years, it has become increasingly obvious that the globe must decrease greenhouse gas emissions by 2050, ideally towards net zero, if we are to fulfil the Paris Agreement’s goal to reduce global temperature increases [ 3 , 4 ]. The net-zero emissions complement the scenario of sustainable development assessment by 2050. According to the agreed scenario of sustainable development, many industrialized economies must achieve net-zero emissions by 2050. However, the net-zero emissions 2050 brought the first detailed International Energy Agency (IEA) modelling of what strategy will be required over the next 10 years to achieve net-zero carbon emissions worldwide by 2050 [ 15–17 ].

The global statistics of greenhouse gas emissions have been identified; in 2019, there was a 1% decrease in CO 2 emissions from the power industry; that figure dropped by 7% in 2020 due to the COVID-19 crisis, thus indicating a drop in coal-fired energy generation that is being squeezed by decreasing energy needs, growth of renewables and the shift away from fossil fuels. As a result, in 2020, the energy industry was expected to generate ~13 Gt CO 2 , representing ~40% of total world energy sector emissions related to CO 2 . The annual electricity generation stepped back to pre-crisis levels by 2021, although due to a changing ‘fuel mix’, the CO 2 emissions in the power sector will grow just a little before remaining roughly steady until 2030 [ 15 ].

Therefore, based on the information mentioned above, the advantages of solar energy technology are a renewable and clean energy source that is plentiful, cheaper costs, less maintenance and environmentally friendly, to name but a few. The significance of this paper is to highlight solar energy applications to ensure sustainable development; thus, it is vital to researchers, engineers and customers alike. The article’s primary aim is to raise public awareness and disseminate the culture of solar energy usage in daily life, since moving forward, it is the best. The scope of this paper is as follows. Section 1 represents a summary of the introduction. Section 2 represents a summary of installed capacity and the application of solar energy worldwide. Section 3 presents the role of solar energy in the sustainable development and employment of renewable energy. Section 4 represents the perspective of solar energy. Finally, Section 5 outlines the conclusions and recommendations for future work.

1.1 Installed capacity of solar energy

The history of solar energy can be traced back to the seventh century when mirrors with solar power were used. In 1893, the photovoltaic (PV) effect was discovered; after many decades, scientists developed this technology for electricity generation [ 18 ]. Based on that, after many years of research and development from scientists worldwide, solar energy technology is classified into two key applications: solar thermal and solar PV.

PV systems convert the Sun’s energy into electricity by utilizing solar panels. These PV devices have quickly become the cheapest option for new electricity generation in numerous world locations due to their ubiquitous deployment. For example, during the period from 2010 to 2018, the cost of generating electricity by solar PV plants decreased by 77%. However, solar PV installed capacity progress expanded 100-fold between 2005 and 2018. Consequently, solar PV has emerged as a key component in the low-carbon sustainable energy system required to provide access to affordable and dependable electricity, assisting in fulfilling the Paris climate agreement and in achieving the 2030 SDG targets [ 19 ].

The installed capacity of solar energy worldwide has been rapidly increased to meet energy demands. The installed capacity of PV technology from 2010 to 2020 increased from 40 334 to 709 674 MW, whereas the installed capacity of concentrated solar power (CSP) applications, which was 1266 MW in 2010, after 10 years had increased to 6479 MW. Therefore, solar PV technology has more deployed installations than CSP applications. So, the stand-alone solar PV and large-scale grid-connected PV plants are widely used worldwide and used in space applications. Fig. 1 represents the installation of solar energy worldwide.

Installation capacity of solar energy worldwide [20].

Installation capacity of solar energy worldwide [ 20 ].

1.2 Application of solar energy

Energy can be obtained directly from the Sun—so-called solar energy. Globally, there has been growth in solar energy applications, as it can be used to generate electricity, desalinate water and generate heat, etc. The taxonomy of applications of solar energy is as follows: (i) PVs and (ii) CSP. Fig. 2 details the taxonomy of solar energy applications.

The taxonomy of solar energy applications.

The taxonomy of solar energy applications.

Solar cells are devices that convert sunlight directly into electricity; typical semiconductor materials are utilized to form a PV solar cell device. These materials’ characteristics are based on atoms with four electrons in their outer orbit or shell. Semiconductor materials are from the periodic table’s group ‘IV’ or a mixture of groups ‘IV’ and ‘II’, the latter known as ‘II–VI’ semiconductors [ 21 ]. Additionally, a periodic table mixture of elements from groups ‘III’ and ‘V’ can create ‘III–V’ materials [ 22 ].

PV devices, sometimes called solar cells, are electronic devices that convert sunlight into electrical power. PVs are also one of the rapidly growing renewable-energy technologies of today. It is therefore anticipated to play a significant role in the long-term world electricity-generating mixture moving forward.

Solar PV systems can be incorporated to supply electricity on a commercial level or installed in smaller clusters for mini-grids or individual usage. Utilizing PV modules to power mini-grids is a great way to offer electricity to those who do not live close to power-transmission lines, especially in developing countries with abundant solar energy resources. In the most recent decade, the cost of producing PV modules has dropped drastically, giving them not only accessibility but sometimes making them the least expensive energy form. PV arrays have a 30-year lifetime and come in various shades based on the type of material utilized in their production.

The most typical method for solar PV desalination technology that is used for desalinating sea or salty water is electrodialysis (ED). Therefore, solar PV modules are directly connected to the desalination process. This technique employs the direct-current electricity to remove salt from the sea or salty water.

The technology of PV–thermal (PV–T) comprises conventional solar PV modules coupled with a thermal collector mounted on the rear side of the PV module to pre-heat domestic hot water. Accordingly, this enables a larger portion of the incident solar energy on the collector to be converted into beneficial electrical and thermal energy.

A zero-energy building is a building that is designed for zero net energy emissions and emits no carbon dioxide. Building-integrated PV (BIPV) technology is coupled with solar energy sources and devices in buildings that are utilized to supply energy needs. Thus, building-integrated PVs utilizing thermal energy (BIPV/T) incorporate creative technologies such as solar cooling [ 23 ].

A PV water-pumping system is typically used to pump water in rural, isolated and desert areas. The system consists of PV modules to power a water pump to the location of water need. The water-pumping rate depends on many factors such as pumping head, solar intensity, etc.

A PV-powered cathodic protection (CP) system is designed to supply a CP system to control the corrosion of a metal surface. This technique is based on the impressive current acquired from PV solar energy systems and is utilized for burying pipelines, tanks, concrete structures, etc.

Concentrated PV (CPV) technology uses either the refractive or the reflective concentrators to increase sunlight to PV cells [ 24 , 25 ]. High-efficiency solar cells are usually used, consisting of many layers of semiconductor materials that stack on top of each other. This technology has an efficiency of >47%. In addition, the devices produce electricity and the heat can be used for other purposes [ 26 , 27 ].

For CSP systems, the solar rays are concentrated using mirrors in this application. These rays will heat a fluid, resulting in steam used to power a turbine and generate electricity. Large-scale power stations employ CSP to generate electricity. A field of mirrors typically redirect rays to a tall thin tower in a CSP power station. Thus, numerous large flat heliostats (mirrors) are used to track the Sun and concentrate its light onto a receiver in power tower systems, sometimes known as central receivers. The hot fluid could be utilized right away to produce steam or stored for later usage. Another of the great benefits of a CSP power station is that it may be built with molten salts to store heat and generate electricity outside of daylight hours.

Mirrored dishes are used in dish engine systems to focus and concentrate sunlight onto a receiver. The dish assembly tracks the Sun’s movement to capture as much solar energy as possible. The engine includes thin tubes that work outside the four-piston cylinders and it opens into the cylinders containing hydrogen or helium gas. The pistons are driven by the expanding gas. Finally, the pistons drive an electric generator by turning a crankshaft.

A further water-treatment technique, using reverse osmosis, depends on the solar-thermal and using solar concentrated power through the parabolic trough technique. The desalination employs CSP technology that utilizes hybrid integration and thermal storage allows continuous operation and is a cost-effective solution. Solar thermal can be used for domestic purposes such as a dryer. In some countries or societies, the so-called food dehydration is traditionally used to preserve some food materials such as meats, fruits and vegetables.

Sustainable energy development is defined as the development of the energy sector in terms of energy generating, distributing and utilizing that are based on sustainability rules [ 28 ]. Energy systems will significantly impact the environment in both developed and developing countries. Consequently, the global sustainable energy system must optimize efficiency and reduce emissions [ 29 ].

The sustainable development scenario is built based on the economic perspective. It also examines what activities will be required to meet shared long-term climate benefits, clean air and energy access targets. The short-term details are based on the IEA’s sustainable recovery strategy, which aims to promote economies and employment through developing a cleaner and more reliable energy infrastructure [ 15 ]. In addition, sustainable development includes utilizing renewable-energy applications, smart-grid technologies, energy security, and energy pricing, and having a sound energy policy [ 29 ].

The demand-side response can help meet the flexibility requirements in electricity systems by moving demand over time. As a result, the integration of renewable technologies for helping facilitate the peak demand is reduced, system stability is maintained, and total costs and CO 2 emissions are reduced. The demand-side response is currently used mostly in Europe and North America, where it is primarily aimed at huge commercial and industrial electricity customers [ 15 ].

International standards are an essential component of high-quality infrastructure. Establishing legislative convergence, increasing competition and supporting innovation will allow participants to take part in a global world PV market [ 30 ]. Numerous additional countries might benefit from more actively engaging in developing global solar PV standards. The leading countries in solar PV manufacturing and deployment have embraced global standards for PV systems and highly contributed to clean-energy development. Additional assistance and capacity-building to enhance quality infrastructure in developing economies might also help support wider implementation and compliance with international solar PV standards. Thus, support can bring legal requirements and frameworks into consistency and give additional impetus for the trade of secure and high-quality solar PV products [ 19 ].

Continuous trade-led dissemination of solar PV and other renewable technologies will strengthen the national infrastructure. For instance, off-grid solar energy alternatives, such as stand-alone systems and mini-grids, could be easily deployed to assist healthcare facilities in improving their degree of services and powering portable testing sites and vaccination coolers. In addition to helping in the immediate medical crisis, trade-led solar PV adoption could aid in the improving economy from the COVID-19 outbreak, not least by providing jobs in the renewable-energy sector, which are estimated to reach >40 million by 2050 [ 19 ].

The framework for energy sustainability development, by the application of solar energy, is one way to achieve that goal. With the large availability of solar energy resources for PV and CSP energy applications, we can move towards energy sustainability. Fig. 3 illustrates plans for solar energy sustainability.

Framework for solar energy applications in energy sustainability.

Framework for solar energy applications in energy sustainability.

The environmental consideration of such applications, including an aspect of the environmental conditions, operating conditions, etc., have been assessed. It is clean, friendly to the environment and also energy-saving. Moreover, this technology has no removable parts, low maintenance procedures and longevity.

Economic and social development are considered by offering job opportunities to the community and providing cheaper energy options. It can also improve people’s income; in turn, living standards will be enhanced. Therefore, energy is paramount, considered to be the most vital element of human life, society’s progress and economic development.

As efforts are made to increase the energy transition towards sustainable energy systems, it is anticipated that the next decade will see a continued booming of solar energy and all clean-energy technology. Scholars worldwide consider research and innovation to be substantial drivers to enhance the potency of such solar application technology.

2.1 Employment from renewable energy

The employment market has also boomed with the deployment of renewable-energy technology. Renewable-energy technology applications have created >12 million jobs worldwide. The solar PV application came as the pioneer, which created >3 million jobs. At the same time, while the solar thermal applications (solar heating and cooling) created >819 000 jobs, the CSP attained >31 000 jobs [ 20 ].

According to the reports, although top markets such as the USA, the EU and China had the highest investment in renewables jobs, other Asian countries have emerged as players in the solar PV panel manufacturers’ industry [ 31 ].

Solar energy employment has offered more employment than other renewable sources. For example, in the developing countries, there was a growth in employment chances in solar applications that powered ‘micro-enterprises’. Hence, it has been significant in eliminating poverty, which is considered the key goal of sustainable energy development. Therefore, solar energy plays a critical part in fulfilling the sustainability targets for a better plant and environment [ 31 , 32 ]. Fig. 4 illustrates distributions of world renewable-energy employment.

World renewable-energy employment [20].

World renewable-energy employment [ 20 ].

The world distribution of PV jobs is disseminated across the continents as follows. There was 70% employment in PV applications available in Asia, while 10% is available in North America, 10% available in South America and 10% availability in Europe. Table 1 details the top 10 countries that have relevant jobs in Asia, North America, South America and Europe.

List of the top 10 countries that created jobs in solar PV applications [ 19 , 33 ]

ContinentCountryPrevalent jobs (millions of jobs)
North AmericaUnited States0.240
AsiaViet Nam0.055
South AmericaBrazil0.040
ContinentCountryPrevalent jobs (millions of jobs)
North AmericaUnited States0.240
AsiaViet Nam0.055
South AmericaBrazil0.040

Solar energy investments can meet energy targets and environmental protection by reducing carbon emissions while having no detrimental influence on the country’s development [ 32 , 34 ]. In countries located in the ‘Sunbelt’, there is huge potential for solar energy, where there is a year-round abundance of solar global horizontal irradiation. Consequently, these countries, including the Middle East, Australia, North Africa, China, the USA and Southern Africa, to name a few, have a lot of potential for solar energy technology. The average yearly solar intensity is >2800 kWh/m 2 and the average daily solar intensity is >7.5 kWh/m 2 . Fig. 5 illustrates the optimum areas for global solar irradiation.

World global solar irradiation map [35].

World global solar irradiation map [ 35 ].

The distribution of solar radiation and its intensity are two important factors that influence the efficiency of solar PV technology and these two parameters vary among different countries. Therefore, it is essential to realize that some solar energy is wasted since it is not utilized. On the other hand, solar radiation is abundant in several countries, especially in developing ones, which makes it invaluable [ 36 , 37 ].

Worldwide, the PV industry has benefited recently from globalization, which has allowed huge improvements in economies of scale, while vertical integration has created strong value chains: as manufacturers source materials from an increasing number of suppliers, prices have dropped while quality has been maintained. Furthermore, the worldwide incorporated PV solar device market is growing fast, creating opportunities enabling solar energy firms to benefit from significant government help with underwriting, subsides, beneficial trading licences and training of a competent workforce, while the increased rivalry has reinforced the motivation to continue investing in research and development, both public and private [ 19 , 33 ].

The global outbreak of COVID-19 has impacted ‘cross-border supply chains’ and those investors working in the renewable-energy sector. As a result, more diversity of solar PV supply-chain processes may be required in the future to enhance long-term flexibility versus exogenous shocks [ 19 , 33 ].

It is vital to establish a well-functioning quality infrastructure to expand the distribution of solar PV technologies beyond borders and make it easier for new enterprises to enter solar PV value chains. In addition, a strong quality infrastructure system is a significant instrument for assisting local firms in meeting the demands of trade markets. Furthermore, high-quality infrastructure can help reduce associated risks with the worldwide PV project value chain, such as underperforming, inefficient and failing goods, limiting the development, improvement and export of these technologies. Governments worldwide are, at various levels, creating quality infrastructure, including the usage of metrology i.e. the science of measurement and its application, regulations, testing procedures, accreditation, certification and market monitoring [ 33 , 38 ].

The perspective is based on a continuous process of technological advancement and learning. Its speed is determined by its deployment, which varies depending on the scenario [ 39 , 40 ]. The expense trends support policy preferences for low-carbon energy sources, particularly in increased energy-alteration scenarios. Emerging technologies are introduced and implemented as quickly as they ever have been before in energy history [ 15 , 33 ].

The CSP stations have been in use since the early 1980s and are currently found all over the world. The CSP power stations in the USA currently produce >800 MW of electricity yearly, which is sufficient to power ~500 000 houses. New CSP heat-transfer fluids being developed can function at ~1288 o C, which is greater than existing fluids, to improve the efficiency of CSP systems and, as a result, to lower the cost of energy generated using this technology. Thus, as a result, CSP is considered to have a bright future, with the ability to offer large-scale renewable energy that can supplement and soon replace traditional electricity-production technologies [ 41 ]. The DESERTEC project has drawn out the possibility of CSP in the Sahara Desert regions. When completed, this investment project will have the world’s biggest energy-generation capacity through the CSP plant, which aims to transport energy from North Africa to Europe [ 42 , 43 ].

The costs of manufacturing materials for PV devices have recently decreased, which is predicted to compensate for the requirements and increase the globe’s electricity demand [ 44 ]. Solar energy is a renewable, clean and environmentally friendly source of energy. Therefore, solar PV application techniques should be widely utilized. Although PV technology has always been under development for a variety of purposes, the fact that PV solar cells convert the radiant energy from the Sun directly into electrical power means it can be applied in space and in terrestrial applications [ 38 , 45 ].

In one way or another, the whole renewable-energy sector has a benefit over other energy industries. A long-term energy development plan needs an energy source that is inexhaustible, virtually accessible and simple to gather. The Sun rises over the horizon every day around the globe and leaves behind ~108–1018 kWh of energy; consequently, it is more than humanity will ever require to fulfil its desire for electricity [ 46 ].

The technology that converts solar radiation into electricity is well known and utilizes PV cells, which are already in use worldwide. In addition, various solar PV technologies are available today, including hybrid solar cells, inorganic solar cells and organic solar cells. So far, solar PV devices made from silicon have led the solar market; however, these PVs have certain drawbacks, such as expenditure of material, time-consuming production, etc. It is important to mention here the operational challenges of solar energy in that it does not work at night, has less output in cloudy weather and does not work in sandstorm conditions. PV battery storage is widely used to reduce the challenges to gain high reliability. Therefore, attempts have been made to find alternative materials to address these constraints. Currently, this domination is challenged by the evolution of the emerging generation of solar PV devices based on perovskite, organic and organic/inorganic hybrid materials.

This paper highlights the significance of sustainable energy development. Solar energy would help steady energy prices and give numerous social, environmental and economic benefits. This has been indicated by solar energy’s contribution to achieving sustainable development through meeting energy demands, creating jobs and protecting the environment. Hence, a paramount critical component of long-term sustainability should be investigated. Based on the current condition of fossil-fuel resources, which are deemed to be depleting energy sources, finding an innovative technique to deploy clean-energy technology is both essential and expected. Notwithstanding, solar energy has yet to reach maturity in development, especially CSP technology. Also, with growing developments in PV systems, there has been a huge rise in demand for PV technology applications all over the globe. Further work needs to be undertaken to develop energy sustainably and consider other clean energy resources. Moreover, a comprehensive experimental and validation process for such applications is required to develop cleaner energy sources to decarbonize our planet.

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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New light-harvesting system offers 38% more efficiency for solar cells

Inspired by arrangement of dyes in plants and bacteria, researchers replicated the arrangement using four dyes and found significant improvements..

Ameya Paleja

Ameya Paleja

New light-harvesting system offers 38% more efficiency for solar cells

The light-harvesting systems used in commercially available solar cells are not very efficient. 

Universitat Wurzburg

Researchers at the Julius-Maximilians-Universität (JMU) in Würzburg, Germany, have designed a novel light-harvesting system that can more efficiently use solar energy by absorbing the entire visible light spectrum. When tested in a research environment, their system converted 38 percent of incident light into fluorescence, a significant leap compared to other systems today. 

Technologies such as solar energy are critical in our bid to move away from fossil fuels. Although solar energy is making global gains in terms of installations, the technology has plenty of scope for improvement in terms of energy efficiency. 

Interesting Engineering has previously reported on how researchers are using various approaches to improve the output generated by a solar cell.

While most of this has focused on the material used to make the solar cell, researchers at JMU looked at the problem from a different angle—the light-harvesting system in solar cells. 

Issues with light-harvesting systems

The light-harvesting systems used in commercially available solar cells are not very efficient. 

They are made from inorganic semiconductor materials such as silicon, and although they are panchromatic and able to absorb the entire spectrum of visible light, their absorbance is very low. 

This is why solar cells need thick silicon layers to absorb more light, making them heavy. 

The researchers were inspired by naturally occurring systems, such as plants and bacteria, which can use a broad spectrum of light for photosynthesis . 

This is achieved using organic dyes, which are much thinner and lighter. When used alone, the organic dyes do not absorb light across a wide spectral range. 

The researchers then tried to replicate the complex arrangement of dyes in naturally occurring systems to develop a highly efficient new light-harvesting system. 

solar energy harvesting research papers

How does the system work? 

The JMU researchers designed a light-harvesting antenna using four different merocyanine dyes. These dyes were folded and stacked upon each other sophisticatedly, allowing ultra-fast and efficient energy transport. 

The prototype light-harvesting system has been dubbed URPB after the wavelengths that the four dye components can absorb – U for ultraviolet, R for red, P for purple, and B for blue. 

To determine how well the light harvesting system performed, the researchers measured its fluorescence quantum yield – the amount of energy the system emits in the form of fluorescence, the press release said. 


The team found that in their special arrangement, the four dyes generated fluorescence from the 38 percent of light incident on them. In comparison, when placed individually, each dye could only convert no more than three percent of the light into fluorescence, showcasing the significant difference the spatial arrangement of dyes makes. 

“Our system has a band structure similar to that of inorganic semiconductors. This means that it absorbs panchromatically over the entire visible range,” said Frank Würthner, a professor of chemistry at JMU. “And it uses the high absorption coefficients of organic dyes. This means that, similar to natural light-harvesting systems, it can absorb a lot of light energy in a relatively thin layer.”

The research findings were published in the journal Chem .

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Ameya Paleja Ameya&nbsp;is a science writer based in Hyderabad, India. A Molecular Biologist at heart, he traded the micropipette to write about science during the pandemic and does not want to go back. He likes to write about genetics, microbes, technology, and public policy.


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  • DOI: 10.48175/ijarsct-18973
  • Corpus ID: 270774686

Design and Implementation of Energy Harvesting and Pollution Control System using Nano Tree

  • Dr. Kalpana A B , Smruthi B , +1 author Somisetti V Tejaswi
  • Published in International Journal of… 26 June 2024
  • Environmental Science, Engineering, Materials Science

9 References

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