Photovoltaic Process Research Articles

Correlation between spin-dependent recombination centers and long-lived polarons generated in organic photovoltaic devices revealed by successive ESR and EDMR measurements

Using a successive detection technique with electron spin resonance (ESR) and electrically detected magnetic resonance (EDMR), this study clarifies the quantitative correlation between photoinduced spin amounts and spin-dependent recombination (SDR) currents in organic photovoltaic devices (OPVs). Using this unique method of sequentially switching between ESR and EDMR measurements under light irradiation, we find that the intensities of light-induced ESR and EDMR spectra increase along with the light irradiation power. Although positive correlation exists between the number of photo-generated radicals and the SDR currents, the relation is not proportional, which demonstrates that most of the photo-generated radicals are residual accumulated charges. Additionally, phases of the EDMR spectra under light irradiation were found to be changed because of a delay of modulated EDMR signals. The phase variation is probably caused by recombination centers: positive polarons that have arrived at the interface between an aluminum electrode and an active layer by charge drifting after charge separation. Because positive polarons are expected to transport positive charges to the opposite-side electrode of the aluminum as a negative charge collector, this leakage current can be a factor of disturbing an optimal charge collection. This combined technique of ESR and EDMR is useful to explore the different roles of polarons in the photovoltaic conversion processes, thereby providing important information for improving the fill factors and open-circuit voltages of the OPVs, which generate long-lived polarons.

Strongly enhanced shift current at exciton resonances in a noncentrosymmetric wide-gap semiconductor

Excitons are fundamental quasiparticles that are ubiquitous in photoexcited semiconductors and insulators. Despite causing a sharp and strong photoabsorption near the interband absorption edge, charge-neutral excitons do not yield photocurrent in conventional photovoltaic processes unless dissociated into free charge carriers. Here, we experimentally demonstrate that excitons can directly contribute to photocurrent generation through a nonlinear light−matter interaction in a noncentrosymmetric semiconductor CuI. Epitaxial thin films of CuI exhibit a substantial enhancement of photocurrent at exciton resonance energies even below the bandgap. From the light polarization dependence, this photocurrent is identified to be shift current, a nonlinear photocurrent driven by the change in the geometric Berry phase of electron wave functions upon the optical transition. The shift current at the exciton resonance is much larger than that induced above the band gap by free electron−hole excitation, and their signs are opposite. First-principles calculations elucidate that the sign and magnitude of the exciton shift current are strongly dependent on the strain in the thin film. The present study reveals the crucial role of excitons in enhancing the shift current magnitude and its strain sensitivity, and will open an unprecedented route for efficient manipulation of nonlinear optical effects.

Nanomaterials for Energy Conversion

Nanomaterials have emerged as a cornerstone in advancing energy conversion technologies, offering unparalleled potential for improving efficiency and sustainability. Their unique properties—such as high surface area, tunable electronic structure, and quantum effects—enable superior performance in applications like solar cells, fuel cells, thermoelectric devices, and batteries. By manipulating nanoscale architectures, researchers can enhance energy harvesting, storage, and conversion processes. This study explores the role of nanomaterials in energy conversion, focusing on their design, synthesis, and application in cutting-edge technologies. The integration of nanomaterials with advanced energy systems has shown significant potential to address challenges such as limited energy resources and environmental concerns. Special emphasis is placed on nanostructured semiconductors, quantum dots, and graphene-based materials for their contributions to photovoltaic and catalytic processes. Despite their advantages, challenges remain, including scalability, stability, and cost-efficiency. This research aims to provide a comprehensive overview of recent advancements, evaluate existing challenges, and highlight future directions for nanomaterials in energy conversion, ultimately contributing to a sustainable energy future.

Molecular models of PM6 for non-fullerene acceptor organic solar cells: How DAD and ADA structures impact charge separation and charge recombination.

The quadrupole moment of a non-fullerene acceptor (NFA) generated by the constituent electron donor (D) and acceptor (A) units is a significant factor that affects the charge separation (CS) and charge recombination (CR) processes in organic photovoltaics (OPVs). However, its impact on p-type polymer domains remains unclear. In this study, we synthesized p-type molecules, namely acceptor-donor-acceptor (ADA) and donor-acceptor-donor (DAD), which are components of the benchmark PM6 polymer (D: benzodithiophene and A: dioxobenzodithiophene). Planar heterojunction films, a model of bulk heterojunction, were prepared using ADA, DAD, and PM6 as the bottom p-type layers and Y6 NFA as the top n-type layer. Flash-photolysis time-resolved microwave conductivity, femtosecond transient absorption spectroscopy, and quantum mechanical calculations were employed to probe the charge carrier dynamics. Our findings reveal that while the subtle difference in quadrupole moment and energy gradient of the p-type materials has a minimal influence on CS, the molecular type (ADA or DAD) significantly affects the bulk CR. This study expands the understanding of how the p-type component and its conformation at the p/n interface impact the CS and CR in OPVs, highlighting the critical role of molecular donors in optimizing device performance.

Enhanced Photovoltaic Efficiency through 3D-Printed COC/Al₂O₃ Anti-Reflective Coversheets

In the past few years, there has been a considerable growth in the utilization of solar cells to produce electrical power to fulfil the growing worldwide energy demands. An optical loss is a crucial factor that impacts the performance of the photovoltaic conversion process. This loss occurs when incoming sunlight is reflected back on the outer layer of the photovoltaic cells. The application of antireflective materials on the photovoltaic substrates can enhance the absorption of sunlight and minimize the reflection. Currently, there is no ideal anti-reflective coating for solar cells that can allow the transmission of sunlight without any reflection. In this research, a transparent cyclic-olefin-copolymer (COC) was used to fabricate anti-reflection (AR) coversheet by fused deposition modelling process (3D printing). Further, the aluminium oxide Al2O3 powder was added at the proportions of 1wt%, 2wt%, 3wt%, and 4wt% to the COC polymer to increase the anti-reflection characteristics. The structural, morphological, mechanical (tear strength), electrical and temperature characteristics were studied. The performance of COC with aluminium oxide (COCA) coversheets was evaluated, and the findings revealed a considerable increment in power conversion efficiency (PCE) of 17.21% (sunlight exposure) and 18.34% (neodymium light) for COCA3 coversheets. As seen, the COCA3 sample exhibit lower electrical resistance of 3.88 ×10-3 Ω cm, carrier concentration (36.91×1020 cm-3) and higher hall mobility (13.67 cm2/Vs). The findings from the investigations demonstrated that the utilization of COC with Al2O3 powder (COCA) can be a suitable antireflective coversheet material for boosting the performance of polycrystalline silicon photovoltaic cells.

Analysis of the Effect of Delay Time and Type of Coolant Media with the Spray Method on the Back Surface of PV Panel

Sunlight can be harnessed as a clean and renewable energy source using solar cells and the photovoltaic process. However, relying on direct sunlight exposure can increase solar cell temperatures and negatively impact performance. This research aims to maintain cell efficiency by exploring the effectiveness of spraying coolant media on the bottom surface of panels through three timed intervals (10, 20, and 30 minutes) using three different media (A, B, and C). Each spray application lasts for 1 minute. Analyzing the test results with Minitab18 software with full factorial design will identify the most effective treatment for maintaining performance. Based on the results of the experimental test, coolant A with a 10-minute delay spraying time has a maximum power of 52.89 Watts, a temperature of 48oC, and an efficiency of 5.69%. Response Optimization using Design of Experiment (DOE) Full Factorial shows an optimal response with coolant A and a 10-minute delay spraying time with the lowest temperature at 49.3oC, maximum output power at 46.87 Watts, and efficiency at 5.61%. Moreover, Tukey Krammer test result provides an information about 10-minute delay spraying time has a better performance to reduce the PV panel temperature compared to 30-minute delay spraying time by 3.94 oC.

A statistical physics-based methodology for evaluating the efficiency of an adsorption refrigeration cycle using C3H2F4/Maxsorb III

Recently, the statistical physics modeling found a great success in the description of various aspects of adsorption phenomena such as olfaction, depollution, and photovoltaic processes. Therefore, we attempted here to apply this treatment in the domain of refrigeration by an adsorption/ desorption process cycle, using the grand canonical formalism of the statistical physics as a working tool. So, for this study, four advanced equations based on statistical physics treatment are used to clarify the adsorption process at molecular level and define its physico-chemical parameters for refrigeration applications. Our results showed that the double layer model with two energies provides useful details concerning this phenomenon by determining the number of captured HFO-1234ze (E) (C3H2F4) molecules per site nAM, the density of receptor site Drs, the adsorption quantity at saturation N a sat and two energetic parameters Phs1 and Phs2. The steric results revealed that the adsorbed C3H2F4 molecules tended towards a parallel position when the temperature increased. Evaluations of the adsorption energies and enthalpy values indicated that the anchoring process is exothermic and of a physisorption nature. In order to improve the thermodynamic efficiency, the coefficient of performance was calculated by analyzing changes in thermodynamic functions, giving a value of 0.58 for the C3H2F4 / Maxsorb III. Also, the COP value can be enhanced by using the same adsorbate with high compressibility to minimize the machine size but changing the present adsorbent by activated carbon derived from biomass such as: Waste Palm Trunk (WPT-AC) and Mangrove wood (M−AC).

Overview of Wind and Photovoltaic Data Stream Classification and Data Drift Issues

The development in the fields of clean energy, particularly wind and photovoltaic power, generates a large amount of data streams, and how to mine valuable information from these data to improve the efficiency of power generation has become a hot spot of current research. Traditional classification algorithms cannot cope with dynamically changing data streams, so data stream classification techniques are particularly important. The current data stream classification techniques mainly include decision trees, neural networks, Bayesian networks, and other methods, which have been applied to wind power and photovoltaic power data processing in existing research. However, the data drift problem is gradually highlighted due to the dynamic change in data, which significantly impacts the performance of classification algorithms. This paper reviews the latest research on data stream classification technology in wind power and photovoltaic applications. It provides a detailed introduction to the data drift problem in machine learning, which significantly affects algorithm performance. The discussion covers covariate drift, prior probability drift, and concept drift, analyzing their potential impact on the practical deployment of data stream classification methods in wind and photovoltaic power sectors. Finally, by analyzing examples for addressing data drift in energy-system data stream classification, the article highlights the future prospects of data drift research in this field and suggests areas for improvement. Combined with the systematic knowledge of data stream classification techniques and data drift handling presented, it offers valuable insights for future research.

Performance prediction of ventilated building-integrated photovoltaic system with lightweight flexible crystalline silicon module based on experimental fitting method

The novel ventilated building-integrated photovoltaic system with lightweight flexible crystalline silicon modules (VL-BIPV) has a self-weight of only about 6 kg/m2, which helps to address weight-bearing challenges on low-capacity industrial building rooftops. However, the unique thermal dissipation features of the system pose challenges for the analysis of its photovoltaic performance and thermal processes. Therefore, a comparative experimental testing approach was employed, comparing the VL-BIPV system with a conventional rooftop photovoltaic system. Using the efficiency equations of the conventional photovoltaic system as a reference, the functional equations were fitted based on experimental results. Based on typical meteorological year data in Nanjing, annual power generation and PV cell temperature variations were evaluated, along with power generation and carbon reduction benefits over a 25-year lifetime. The results indicate that the VL-BIPV system achieves an annual power generation of 262.22 kWh/m2, a 6.52% increase compared to the conventional system. Additionally, the highest average and maximum cell temperatures in the VL-BIPV system during the year are 58.39 °C and 64.74 °C, respectively, both lower than the conventional system's peak at 68.34 °C. Over a 25-year lifespan, the VL-BIPV system reduces carbon emissions by 20.17 kgCO2/Wp, exceeding the conventional system's reduction by 1.25 kgCO2/Wp.

Research on prediction method of photovoltaic power generation based on transformer model

Accurate prediction of photovoltaic power generation is of great significance to stable operation of power system. To improve the prediction accuracy of photovoltaic power, a photovoltaic power generation prediction machine learning model based on Transformer model is proposed in this paper. In this paper, the basic principle of Transformer model is introduced. Correlation analysis tools such as Pearson correlation coefficient and Spearman correlation coefficient are introduced to analyze the correlation between various factors and power generation in the photovoltaic power generation process. Then, the prediction results of traditional machine learning models and the Transformer model proposed in this paper were compared and analyzed for errors. The results show that: for long-term prediction tasks such as photovoltaic power generation prediction, Transformer model has higher prediction accuracy than traditional machine learning models. Moreover, compared with BP, LSTM and Bi-LSTM models, the Mean Square Error (MSE) of Transformer model decreases by 70.16%, 69.32% and 62.88% respectively in short-term prediction, and the Mean Square Error (MSE) of Transformer model decreases by 63.58%, 51.02% and 38.3% respectively in long-term prediction, which has good prediction effect. In addition, compared with the long-term prediction effect of Informer model, Transformer model has higher prediction accuracy.

Increasing efficiency in perovskite solar cells through energy downconversion using nanoparticles

The generation of electricity through solar energy stands out as one of the fastest-growing renewable energy sources worldwide. The global demand for energy, coupled with the rising levels of CO2 in the atmosphere, highlights the need to develop renewable sources of energy. The current challenge of utilizing photovoltaic cells for electricity production lies in the efficiency of the photovoltaic conversion process occurring within the device. To enhance the efficiency of photovoltaic devices, scientists have been investigating light conversion processes to improve radiation absorption and conversion. The study and application of the downconversion process with luminescent materials have been explored to prevent energy loss in solar devices. Zinc Oxide nanoparticles (ZnO NPs) have been employed for this purpose due to their favorable optical properties for downconversion applications. Organic–inorganic perovskites, such as methylammonium lead iodide perovskite (CH3NH3PbI3), have shown promising results for power conversion efficiency, and the present study aimed to assess the increased efficiency of perovskite devices with the addition of ZnO nanoparticles on top of the device. Such optical improvement in the device can occur because high-energy photons, which would be absorbed by layers such as glass and ITO, which are not semiconductors and do not generate charge carriers, will now be absorbed by the ZnO NPs and emitted at longer wavelengths. These longer wavelengths will be absorbed in the active layer of the device, thus generating charge carriers. The obtained results demonstrated that ZnO nanoparticles with approximately 45 nm diameter can successfully produce a downconversion process, leading to improved light absorption by the active layer of the cell and consequently improving photovoltaic efficiency of a perovskite device by 5.5 %.

Singlet fission photovoltaic cells as dual-wavelength laser power converters compatible with highly efficient solar cells

I applied photovoltaic cells equipped with singlet fission (SF) of molecular systems to dual-wavelength laser power converters (DW-LPCs) that efficiently convert two laser lights of different wavelengths to electricity. When the SF-DW-LPC is illuminated by eye-safe laser light of 1470 nm wavelength emitted from a laser diode, a single photon is converted to a single carrier. On the other hand, a single high-energy photon emitted from a high-power and low-cost laser diode of 808 nm is converted to two carriers by SF owing to its endothermic feature, even though the photon energy is slightly lower than twice the fundamental energy gap. Furthermore, the SF-DW-LPC operates as a highly efficient solar cell. These functions are required for optical wireless power transmission to moving objects including electric vehicles and flying drones. I modeled the photovoltaic process with SF and evaluated the limiting conversion efficiencies by detailed-balance calculations. Conversion efficiencies of the SF-DW-LPC for these two laser lights are competitive with those of the conventional single-junction LPCs dedicated to these wavelengths, respectively. The efficiency under solar light is close to that of the optimally designed SF solar cell. Furthermore, the SF-DW-LPC outperforms other types of DW-LPCs designed on the basis of intermediate band, triplet–triplet annihilation, and multiple exciton generation solar cells. Endothermic SF and carrier/energy extraction into the neighboring acceptors have already been demonstrated. However, molecular systems that apply to 1470 nm have not yet been realized, which is the top-priority issue to be solved to realize highly efficient SF-DW-LPCs.

Influence of choline chloride pH adjuster on physical and optical properties of CuInSe2 thin films by CBD

We concentrate on low-cost thin film solar cells with the help of choline chloride acting as a pH adjuster because copper ternary material compounds and their alloys have become more and more lavish over the past 20 years. Chemical Bath Deposition (CBD) is used to produce copper indium selenium (CuInSe2) thin films on glass substrates while a pH adjuster is present. The micro structural and elemental properties are studied using X-ray diffraction (XRD), Raman, Scanning Electron Microscope (SEM), and Energy Dispersive Spectroscopy (EDS). The optical studies of CuInSe2 thin films are done using a UV–Vis–NIR spectrometer. XRD and Raman spectra shows that the CIS thin films deposited in the sequence of Glass/In/Se/Cu2Se/Se (labeled as CISB) and annealed at 400 °C (CISB400) contains all the peaks corresponding to the chalcopyrite CuInSe2. From the EDS measurements it was noted that the selenium to metal ratio (Se/Cu + In) for all the samples changes with temperature. It is worth to note that the film thickness decreases with a decrease in the pH of choline chloride which results a change in the optical band gap of the films. The optical properties of these thin films are greatly influenced by adjusting pH and it gives better results for optoelectronic devices with a sustainable UV filter which absorbs more in ultraviolet vicinity. The energy gap (Eg) values of the CISB samples were determined and is found to be suitable for photovoltaic conversion process.

The symmetrical differential Buck–Boost DC‐DC converter with partial power processing applied to photovoltaic systems

SummaryThis paper presents the symmetric differential Buck–Boost converter, obtained from the differential connection between two Buck–Boost converters. The topology is characterized by presenting low‐voltage ripple, partial power processing, ability to provide intermediate voltage gains (1 < M < 10) and design simplicity. In this configuration, the output voltage is composed of the sum of the input voltage and the output voltages of both converters differentially connected. The topology description along with its main equations and waveforms in continuous conduction mode with phase‐shift modulation are presented in this paper. Moreover, a mathematical analysis that demonstrates its capability of performing with partial power processing and a comparative analysis with similar topologies are also presented. In addition, experimental results extracted from a prototype are presented and discussed. Because the proposed converter is applied to photovoltaic energy processing, it is validated in open loop and as maximum power point tracker, in which the obtained CEC efficiency is 97.8%.

Revealing the Role of Polyacrylonitrile for Highly Efficient and Stable Perovskite Solar Cells at Extremely Low Temperatures

AbstractPerovskite solar cells (PSCs) are considered to be a promising candidate for near‐space applications due to their high specific power, excellent radiation resistance, and compatibility with flexible substrates. Nevertheless, the low‐temperature cycles impose considerable challenges to their long‐term stability. Herein, the extremely low‐temperature photovoltaic process of PSCs is investigated and the effect mechanisms of PAN (Polyacrylonitrile) as an additive are revealed. It is found that the additive PAN regulates perovskite lattice stress and stabilizes perovskite lattice structure and thus retards perovskite phase transition at low temperatures. Moreover, the PAN improves perovskite dielectric properties and ion migration activation energy and effectively passivates perovskite defects at low temperatures. Therefore, the carrier transport/extraction/collection abilities at low temperatures are enhanced effectively by alleviating exciton effect and increasing dielectric screening effect. Benefiting from these positive effects of PAN, the PAN‐modified device achieves a maximum efficiency of 24.34% at 150K and maintains 72% of its initial value after 120 thermal cycles (290‐130K). Also, the PAN‐modified flexible devices exhibit excellent bending stability and thermal cycle stability. This study provides a deep insight into understanding the extremely low‐temperature photovoltaic behavior of PSCs and demonstrates the potentiality of PAN additive strategy for achieving efficient and stable PSCs at low temperatures.

Multi-Task neural network model considering low power output risk for short-term photovoltaic forecasting

As the proportion of installed photovoltaic power generation continues to increase, low output under the influence of large-scale weather systems has an increasingly significant influence on the power grid. There is an urgent need to improve photovoltaic power generation forecasting accuracy and reduce the risk of insufficient output in forecast results. To this end, a multi-task neural network model considering low power output risk (LPOMTN) for short-term photovoltaic forecasting is proposed. First, a numerical weather forecast encoder based on multi-scale CNN-Attention is established, which can extract multi-time scale photovoltaic output characteristics. Then a low-output day prediction module based on parallel CNN decoding and a photovoltaic low-output process prediction module based on the clear-sky model and LSTM were established. The latter uses the prediction results of the former as input, and the two modules perform training modeling in a multi-task learning manner to strengthen the model’s sensitivity to low-output states and improve the accuracy of low-output process predictions. The calculation example results show that LPOMTN has higher average forecasting accuracy for power output processes compared to methods such as XGBoost.

The role of isomeric alkyl chains of polymer donors on charge-transfer processes in non-fullerene organic photovoltaics: a theoretical investigation

The role of isomeric alkyl chains of polymer donors on charge-transfer processes in non-fullerene organic photovoltaics: a theoretical investigation

A benzobisoxazole-based polymer assisting high efficiency polymer solar cells

Ternary strategy is an effective method to optimize photovoltaic process and improve device performance. In this work, a benzobisoxazole-based conjugated polymer PBBO was designed and employed as the guest component for PM6:BTP-eC9 based devices. The polymer PBBO shows a large band gap of 2.16 eV and strong photoluminescence property, with efficient energy transfer observed between PBBO and PM6. The ternary blends not only form a cascaded energy level distribution, but also an alloy-like structure, which can effectively promote the exciton dissociation, charge transfer and efficiently suppress charge recombination. As a result, the power conversion efficiency (PCE) of the ternary device is substantially improved from 17.50% to 18.66% with the incorporation of PBBO guest component. Meanwhile, the ternary device shows good stability with retaining 86% of initial efficiency after storage for 2000 hours. This research demonstrates that the polymer PBBO would be an excellent photovoltaic material to optimize the performance of polymer solar cells.

Advances in Organic Photovoltaic Cells: Fine‐Tuning of the Photovoltaic Processes

This work highlights recent advancements in how the structures and chemical makeups of the active layer materials affect photovoltaic processes and performance in terms of power conversion efficiency and stability. It further sheds light on the performance optimization of organic photovoltaic cell (OPV) and the relationship between these optimization conditions and OPVs performance. The use of different substituents on the same donor or acceptor material has different optimal conditions. Furthermore, it is shown that the addition of different third components in the active layer has different optimal concentration points. This review also highlights and suggests a possible way to improve the stability of OPV through modification of the active layer. To date, some studies showed that incorporation of the third component in the active layer leads to over 97% stability after more than 1000 h.

Thermodynamic analysis of a spectrum splitting photovoltaic-thermoelectric system with a multi-physics coupling process

Quantifying the losses in the various energy transfer and conversion processes is essential for the study and application of the spectrum splitting photovoltaic-thermoelectric system because of the complex system structure. This paper develops a novel optical-electrical-thermal multi-physics coupling model for the spectrum splitting photovoltaic-thermoelectric system that correlates microstructure and macroscopic properties. Based on the multi-physics coupled model, a thermodynamic analysis method is employed to reveal the distributions of energy and exergy losses in the system, and the effect of the concentration ratio on the losses is investigated. The results show that the optical loss caused by the concentrating and splitting processes accounts for 33.3% of the total input energy. The losses in the energy conversion process are the largest of all the subsystem losses. The energy and exergy losses in the photovoltaic conversion process are 51.5% and 45.9%, while those for the thermoelectric conversion process are 87.8% and 74.5%, respectively. Raising the concentration ratio is an effective way to reduce the exergy loss in the energy conversion process, but it will also partially raise the exergy losses in the heat dissipation and cooling processes. The exergy efficiency of the SSPV-TE system can reach 21.75% at a concentration ratio of 500. The results are important for better understanding the operating principles, optimizing the performance, and advancing the application of the spectrum splitting photovoltaic-thermoelectric system.