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The synthesis of fuels using sunlight offers a promising sustainable solution for chemical energy storage, but inefficient utilization of the solar spectrum limits its commercial viability. Apart from fundamental improvements to (photo)catalyst materials, solar fuel production systems can also be designed to improve solar energy utilization by integrating complementary technologies that more efficiently utilize the solar spectrum. Here we review recent progress on emerging complementary approaches to better modify, enhance or distribute solar energy for sunlight-to-fuel conversion, including advanced light management, integrated thermal approaches and solar concentrators. These strategies can improve the efficiency and production rates of existing photo(electro)chemical systems and, therefore, the overall economics of solar fuel production. More broadly, the approaches highlight the necessary collaboration between materials science and engineering to help drive the adoption of a sustainable energy economy using existing technologies. Fuels synthesized using sunlight offer a sustainable solution for chemical energy storage, but inefficient utilization of the solar spectrum has limited their broader viability. This Review looks at how approaches that are complementary to one another can be employed to better exploit solar energy for sunlight-to-fuel conversion.

Aviation and shipping currently contribute approximately 8% of total anthropogenic CO 2 emissions, with growth in tourism and global trade projected to increase this contribution further 1 – 3 . Carbon-neutral transportation is feasible with electric motors powered by rechargeable batteries, but is challenging, if not impossible, for long-haul commercial travel, particularly air travel 4 . A promising solution are drop-in fuels (synthetic alternatives for petroleum-derived liquid hydrocarbon fuels such as kerosene, gasoline or diesel) made from H 2 O and CO 2 by solar-driven processes 5 – 7 . Among the many possible approaches, the thermochemical path using concentrated solar radiation as the source of high-temperature process heat offers potentially high production rates and efficiencies 8 , and can deliver truly carbon-neutral fuels if the required CO 2 is obtained directly from atmospheric air 9 . If H 2 O is also extracted from air 10 , feedstock sourcing and fuel production can be colocated in desert regions with high solar irradiation and limited access to water resources. While individual steps of such a scheme have been implemented, here we demonstrate the operation of the entire thermochemical solar fuel production chain, from H 2 O and CO 2 captured directly from ambient air to the synthesis of drop-in transportation fuels (for example, methanol and kerosene), with a modular 5 kW thermal pilot-scale solar system operated under field conditions. We further identify the research and development efforts and discuss the economic viability and policies required to bring these solar fuels to market. Carbon-neutral hydrocarbon fuels can be produced using sunlight and air via a thermochemical solar fuel production chain, thus representing a pathway towards the long-term decarbonization of the aviation sector.

This paper presents a solar cell crack detection system for use in photovoltaic (PV) assembly units. The system utilizes four different Convolutional Neural Network (CNN) architectures with varying validation accuracy to detect cracks, microcracks, Potential Induced Degradations (PIDs), and shaded areas. The system examines the electroluminescence (EL) image of a solar cell and determines its acceptance or rejection status based on the presence and size of the crack. The proposed system was tested on various solar cells and achieved a high degree of accuracy, with an acceptance rate of up to 99.5%. The system was validated with thermal testing using real-world cases, such as shaded areas and microcracks, which were accurately predicted by the system. The results show that the proposed system is a valuable tool for evaluating the condition of PV cells and can lead to improved efficiency. The study also shows that the proposed CNN model outperforms previous studies and can have significant implications for the PV industry by reducing the number of defective cells and improving the overall efficiency of PV assembly units.

The sustainable production of chemicals and fuels from abundant solar energy and renewable carbon sources provides a promising route to reduce climate-changing CO 2 emissions and our dependence on fossil resources. Here, we demonstrate solar-powered formate production from readily available biomass wastes and CO 2 feedstocks via photoelectrochemistry. Non-precious NiOOH/α-Fe 2 O 3 and Bi/GaN/Si wafer were used as photoanode and photocathode, respectively. Concurrent photoanodic biomass oxidation and photocathodic CO 2 reduction towards formate with high Faradaic efficiencies over 85% were achieved at both photoelectrodes. The integrated biomass-CO 2 photoelectrolysis system reduces the cell voltage by 32% due to the thermodynamically favorable biomass oxidation over conventional water oxidation. Moreover, we show solar-driven formate production with a record-high yield of 23.3 μmol cm −2 h −1 as well as high robustness using the hybrid photoelectrode system. The present work opens opportunities for sustainable chemical and fuel production using abundant and renewable resources on earth—sunlight, biomass and CO 2 . The sustainable production of chemicals and fuels from waste carbon sources holds significant promise for reducing our dependence on fossil resources. Here, the authors demonstrate renewable formate production from biomass wastes and CO2 powered by solar energy.

Improving the long-term stability of perovskite solar cells is critical to the deployment of this technology. Despite the great emphasis laid on stability-related investigations, publications lack consistency in experimental procedures and parameters reported. It is therefore challenging to reproduce and compare results and thereby develop a deep understanding of degradation mechanisms. Here, we report a consensus between researchers in the field on procedures for testing perovskite solar cell stability, which are based on the International Summit on Organic Photovoltaic Stability (ISOS) protocols. We propose additional procedures to account for properties specific to PSCs such as ion redistribution under electric fields, reversible degradation and to distinguish ambient-induced degradation from other stress factors. These protocols are not intended as a replacement of the existing qualification standards, but rather they aim to unify the stability assessment and to understand failure modes. Finally, we identify key procedural information which we suggest reporting in publications to improve reproducibility and enable large data set analysis. Reliability of stability data for perovskite solar cells is undermined by a lack of consistency in the test conditions and reporting. This Consensus Statement outlines practices for testing and reporting stability tailoring ISOS protocols for perovskite devices.

The simulation of ideal and non-ideal conditions using the SCAPS-1D simulator for novel structure Ag/FTO/CuBi 2 O 4 /GQD/Au was done for the first time. The recombination of charge carriers in CuBi 2 O 4 is an inherent problem due to very low hole mobility and polaron transport in the valence band. The in-depth analysis of the simulation result revealed that Graphene Quantum Dots (GQDs) can act as an appropriate hole transport layer (HTL) and can enhance hole transportation. The simulation was done under ideal and nonideal conditions. The non-ideal conditions include parasitic resistances, reflection losses, radiative, and Auger recombination whereas the ideal condition was studied without the inclusion of any losses. Under ideal conditions, the cell Ag/FTO/CuBi 2 O 4 /GQD/Au exhibited a photovoltaic (PV) parameter such as open circuit voltage (V oc ), short circuit current (J sc ), fill factor (FF), photo conversion efficiency (PCE) are 1.39 V, 25.898 mA/cm 2 , 90.92%, and 32.79%, respectively. The effect of various cell parameters such as the thickness of the absorber layer, HTL layer, and FTO, acceptor and defect density, the bandgap of the absorber and HTL layer, series and shunt resistance, back and front contact materials, radiation and Auger recombination of the absorber layer, reflection losses on the efficiency of the proposed cell is analysed. The drastic reduction in all PV parameters was observed under non-ideal conditions and the PV parameters are V oc (1.22 V), J sc (2.904 mA/cm 2 ), FF (86.3), and PCE of 3.06%. The charge kinetics such as impedance, conductivity, and capacitance plots, and possible reasons for reductions in PV parameters are discussed in detail.

The efficiency of photovoltaic (PV) systems is often compromised by undetected faults, exacerbated by the complexity of thermal imagery backgrounds. This study presents a novel deep-learning-based approach to enhance fault detection in PV systems by customizing the Atrous Spatial Pyramid Pooling (ASPP) module within a U-Net architecture. We propose and evaluate three modified configurations U-Net-ASPP_Cent, U-Net-ASPP_Diag, and U-Net-ASPP_Hybrid each designed to address specific fault localization challenges, including central and diagonal fault patterns. These configurations aim to overcome the limitations of conventional U-Net-ASPP by enhancing multiscale feature extraction and improving segmentation accuracy in complex PV thermal images. The U-Net-ASPP_Hybrid configuration demonstrated the most balanced performance across all key metrics, achieving a 1.13% improvement in F1-score, a 3.01% increase in Intersection over Union (IoU), and a 9.86% reduction in loss compared to the baseline U-Net-ASPP. Additionally, the U-Net-ASPP_Cent and U-Net-ASPP_Diag configurations provided IoU gains of 1.18% and 1.96%, respectively, while also reducing false positive rates. These results highlight the effectiveness of incorporating region-specific dilation strategies, enhancing the model’s ability to detect diverse and challenging fault patterns in complex thermal imagery. Beyond quantitative performance, qualitative segmentation analysis confirms that the U-Net-ASPP_Hybrid model offers superior fault localization and adaptability to real-world PV inspections. The U-Net-ASPP_Cent model is particularly effective for central anomaly detection, while the U-Net-ASPP_Diag model excels at identifying directional faults such as cracks. The U-Net-ASPP_Hybrid model, combining both strategies, provides a comprehensive solution for automated PV fault detection. These findings underscore the stability, scalability, and real-world applicability of the proposed models, making them ideal for automated PV inspection systems aimed at minimizing manual intervention and enhancing the reliability of renewable energy infrastructure. Future research will explore adaptive dilation strategies and more diverse datasets to further improve model generalization across varying PV environments.

The global demand for electrical energy has witnessed a substantial increase, presenting a challenge for power systems worldwide. In addition to technical considerations, the escalating issue of global warming has become a paramount concern in the planning studies of various sectors. The formulation and resolution of a single-objective non-linear optimization problem are carried out, considering different operational scenarios. Recent heuristic algorithms, including Particle Swarm Optimization (PSO), Cat Swarm Optimization (CSO), Teaching Learning Based Optimization (TLBO), Grey Wolf Optimization (GWO) and Chimp Optimization algorithm (ChOA) are employed to address the complexities associated with maximizing power output under partial shading conditions in solar PV systems. The inherent challenges of achieving MPPT under such conditions make conventional analytic approaches computationally intensive. Hence, this study leverages heuristic algorithms to optimize solar PV system performance, providing efficient solutions to the associated optimization problems. The current research work was performed on a test system using a MATLAB/SIMULINK environment and the results are presented and discussed. From the simulation results, it was found that ChOA have shown higher conversion efficiency of 99.63% with maximum power output of 525.13 W when compared to other optimization algorithms for the given shading pattern condition. Further, ChOA offers easy implementation and faster convergence, outperforming established methods in GMPP search by reducing power oscillations and achieving precise MPP convergence.

Carbon-based perovskite solar cells exhibit a promising application prospect due to its cost effective and attractive hydrophobic nature and chemical inertness, but are still limited to unsatisfied device efficiency. Herein, we design a triple-layer full-carbon electrode for n-i-p typed perovskite solar cells, which is comprised of a modified macroporous carbon layer, a highly conductive graphite layer and a thin dense carbon layer, and each layer undertakes different contribution to improving the cell performance. Based on this full-carbon electrode, inorganic CsPbI 3 perovskite solar cells exhibit >19% certified efficiency which is the highest result among carbon-based CsPbI 3 devices. On one hand, carbon quantum dots decorated on the macro-porous carbon layer can realize better energy alignment of full-carbon electrode/spiro-OMeTAD/CsPbI 3 interface, on the other hand, highly conductive graphite layer is advantageous to carrier transporting. Typically, the top dense carbon layer exhibits significant thermal radiation ability, which can reduce the operational temperature of devices by about 10 °C, both from theoretical simulation and experimental testing. Thereby, packaged full-carbon electrode based CsPbI 3 cells exhibit much better photothermal stability at ~70°C accompanied by white light emitting diode illumination, which exhibit no efficiency degradation after 2000 h continuous operational tracking. The device efficiency of carbon-based perovskite solar cells remains unsatisfactory. Here, the authors design a triple-layer full-carbon electrode with carbon quantum dots decorated on macro-porous carbon layer, realizing certified efficiency of over 19% for n-i-p CsPbI3 perovskite solar cells.

The stoichiometric photocatalytic reaction of CO 2 with H 2 O is one of the great challenges in photocatalysis. Here, we construct a Cu 2 O-Pt/SiC/IrO x composite by a controlled photodeposition and then an artificial photosynthetic system with Nafion membrane as diaphragm separating reduction and oxidation half-reactions. The artificial system exhibits excellent photocatalytic performance for CO 2 reduction to HCOOH and H 2 O oxidation to O 2 under visible light irradiation. The yields of HCOOH and O 2 meet almost stoichiometric ratio and are as high as 896.7 and 440.7 μmol g −1  h −1 , respectively. The high efficiencies of CO 2 reduction and H 2 O oxidation in the artificial system are attributed to both the direct Z-scheme electronic structure of Cu 2 O-Pt/SiC/IrO x and the indirect Z-scheme spatially separated reduction and oxidation units, which greatly prolong lifetime of photogenerated electrons and holes and prevent the backward reaction of products. This work provides an effective and feasible strategy to increase the efficiency of artificial photosynthesis. The stoichiometric photoreaction of CO 2 with H 2 O is one of the big challenges in photocatalysis. An artificial photosynthetic system based on a direct and indirect Z-scheme heterostructure is synthesised, enabling simultaneous CO 2 reduction to HCOOH and H 2 O oxidation to O 2 .