Search Results (2,930)

Dye-sensitized solar cells (DSSCs) remain promising low-cost photovoltaic technologies because of their simple fabrication, tunable optical properties, and effective operation under low-light conditions. Titanium dioxide (TiO₂) is the most widely used photoanode material in DSSCs owing to its chemical stability, suitable band alignment, low toxicity, and excellent transparency. However, the photovoltaic performance and long-term stability of TiO₂-based DSSCs are still limited by charge recombination, slow electron transport, interfacial losses, and structural degradation. This review summarizes recent advances in defect engineering, morphology engineering, and interface engineering of TiO₂ photoanodes for high-performance DSSCs. Attention is given to the role of oxygen vacancies, Ti³⁺ states, metal/non-metal doping, and heterostructure formation in tailoring the electronic structure and charge transport behavior of TiO₂. The influence of various TiO₂ nanostructures, including nanoparticles, nanotubes, nanorods, nanosheets, and hierarchical architectures, on dye adsorption, light scattering, electron mobility, and recombination dynamics is critically discussed. Furthermore, recent progress in interface engineering strategies such as passivation layers, blocking layers, MXene incorporation, composite photoanodes, and atomic layer deposition are examined in relation to interfacial charge transfer and device stability. Current challenges involving defect-induced recombination, morphology-related transport trade-offs, and long-term degradation are also analyzed. Finally, future perspectives on hierarchical nanoarchitectures, multifunctional interfaces, flexible DSSCs, and hybrid TiO₂ systems are presented. This review provides an integrated understanding of how defect, morphology, and interface engineering collectively govern the performance of TiO₂ photoanodes and offers design guidelines for next-generation high-efficiency and stable DSSCs. Full article

Space missions working under harsh heliocentric conditions demand more efficient photovoltaics operating under high solar concentration, high temperatures, and harsh radiation conditions. Although most simulation work has been conducted using the terrestrial AM1.5 spectrum, AM0 high concentrators are of great importance to realistic satellite missions. Though III–V multijunction solar cells are currently the norm in space applications, their efficiency under extremely high solar concentration ratios is not yet optimized to support future space missions. This work designs and numerically optimizes a GaAs VTJ solar cell using SILVACO ATLAS software (5.40.0.R). In the optimization, the thickness of the front and back layers, as well as the doping profile within the emitter, base, and tunnel junction regions, were adjusted. The important PV semiconductor attributes, including the short-circuit current density (Jsc), open-circuit voltage (Voc), fill factor (FF), and efficiency (η), were examined over a concentration factor ranging between 1 and 10,000 suns. The efficiency of the optimized VTJ solar cell increased from 20.4% at 1 sun to 26.0% at 10,000 suns. This is mainly due to the near-linear increase in Jsc and the stable FF, which remains between 87% and 89%. In addition, the solar cell shows a steady increase in Voc between 1.85 V and 2.33 V. An optimized GaAs VTJ solar cell design is a promising component in future space missions, which require high power density and are suited to operating under high heliocentric orbits, such as in the Parker Solar Probe and solar-electric propulsion systems. Full article

CsPbI₂Br, an all-inorganic cesium–lead mixed-halide perovskite, has established itself as a leading contender for next-generation photovoltaics, owing to its near-optimal direct bandgap, exceptional thermal stability, and favorable optoelectronic characteristics. These attributes make it a versatile candidate for both high-efficiency single-junction devices and wide-bandgap top cells in tandem architectures with silicon or low-bandgap perovskites. However, the commercialization of CsPbI₂Br perovskite solar cells (PSCs) is severely hindered by inherent interfacial challenges, including halide segregation under operational stress, high density of interfacial defects, energy-level misalignment between the perovskite and charge transport layers (CTLs), and chemical incompatibility at hetero-interfaces. These factors limit power conversion efficiency (PCE) and long-term operational stability. Interface engineering has thus become the pivotal strategy to address these bottlenecks, enabling transformative improvements in device performance. This review comprehensively summarizes the state-of-the-art interface engineering strategies for CsPbI₂Br PSCs, including molecular passivation, construction of 2D/3D heterostructures, design of composite interlayers, and development of dopant-free, stable CTLs. The underlying mechanisms of defect passivation, non-radiative recombination suppression, energy-level alignment optimization, and ion migration inhibition are systematically elucidated. Furthermore, we discuss critical remaining challenges, including the trade-off between phase stability and optoelectronic quality, interfacial delamination due to thermal expansion mismatch, and scalable fabrication of interface-modified large-area devices. Finally, future research directions are proposed, emphasizing the development of multifunctional interfacial materials, all-inorganic interface architectures, in situ characterization combined with computational modeling, and integration into tandem photovoltaic systems. By consolidating current knowledge and highlighting promising frontiers, this review aims to guide the rational design of high-performance, stable, and commercially viable CsPbI₂Br PSCs, accelerating their role in the global transition toward renewable energy. Full article

This study examines the influence of device architecture and substrate materials on the external quantum efficiency (EQE) of high-performance solar cells. A diverse array of photovoltaic technologies was evaluated, including formamidinium lead iodide CH₅N₂PbI₃ (FAPI) perovskite cells and various silicon-based designs, such as Passivated Emitter and Rear Cell (PERC), Back Integrated Contact (BIC), and Bifacial structures. Quantum characteristics were determined through wavelength-dependent photocurrent measurements utilizing a precision monochromator system. Our results reveal that device structure is a primary determinant of charge carrier collection efficiency; specifically, Bifacial and PERCs achieved superior short-circuit current densities (J_sc) of 40.98 mA/cm² and 39.42 mA/cm², respectively. Notably, the EQE(λ) profile of Bifacial cells under n-side illumination exhibits a near-ideal rectangular shape, indicating an optimized spectral response throughout the operating spectrum. Furthermore, the analysis investigates the role of surface recombination velocity and the efficacy of advanced passivation layers—specifically Al₂O₃ and SiN_x—in enhancing quantum performance by mitigating recombination state density. Our findings demonstrate that the strategic integration of advanced passivation layers (Al₂O₃ and SiN_x) with optimized architectures, such as PERC and Bifacial designs, is paramount for maximizing charge carrier collection and achieving record-high current densities reaching 40.98 mA/cm². A comprehensive analysis of solar cell performance involves spectral response (SR) and external quantum efficiency (EQE) as functions of wavelength. Additionally, SR-based current density analysis enables more accurate evaluation of cell parameters than standard I–V characterization. Full article

As an emerging photovoltaic technology, Cu₂ZnSn(S,Se)₄ (CZTSSe) thin-film solar cells are regarded as a viable, cost-effective alternative to satisfy future demand for green energy. This promise is attributed to their tunable bandgap (1.0~1.5 eV), high absorption coefficient (>10⁴ cm⁻¹), and environmentally friendly composition. Currently, the record power conversion efficiency (PCE) of CZTSSe devices has reached 16.6%, approaching commercial levels. However, this value remains significantly lower than its theoretical limit of 32.8% and the 23.6% achieved by the homologous CIGS technology, indicating immense potential for performance enhancement. The severe open-circuit voltage deficit (E_g/q-V_oc) remains a critical factor preventing CZTSSe solar cells from reaching their expected efficiency. This issue is primarily associated with band misalignment and deep-level defects at the interfaces. At present, interface engineering has been demonstrated to be an effective strategy to significantly improve the performance of CZTSSe thin-film solar cells. Herein, we review the development process of CZTSSe photovoltaics, systematically discuss existing interface-related issues and comprehensively summarize recent strategies in interface engineering. Finally, to further elucidate the intrinsic mechanisms and facilitate the development of high-efficiency devices, future research directions and perspectives regarding interface engineering are proposed. Full article

This study investigates an integrated ecological strategy to reduce electricity consumption in semi-collective housing located in the hot–arid climate of Biskra, Algeria, a region with high solar potential. The research combines photovoltaic (PV) electricity generation with passive thermal insulation using a locally sourced bio-based material derived from date palm fibers. The case study includes 104 dwellings within a residential complex of 350 units. Results show that monocrystalline PV panels (350 W) can produce approximately 479 kWh/panel/year. To meet the total annual electricity demand (504,712 kWh), around 1052 panels are required, corresponding to 1714 m² (13.8%) of the available building envelope. This installation area demonstrates the significant photovoltaic potential of the residential complex under hot–arid climatic conditions. Thermal analysis indicates that integrating a 5 cm palm fiber insulation layer increases thermal resistance from 2.06 to 2.62 m²·°C/W and reduces heat flux from 2.18 to 1.72 W/m². This improvement decreases conductive heat transfer through the envelope by approximately 21%, while numerical simulations indicate indoor temperature reductions of 4–8 °C during summer conditions. These findings demonstrate that combining PV systems with bio-based insulation significantly enhances energy efficiency and thermal comfort in residential buildings under desert climatic conditions. Full article

The present study investigates the application of electric resistive heating to photovoltaic (PV) panels, aimed at enabling their subsequent thermo-mechanical delamination. The key process parameters—namely current magnitude and applied voltage—required for direct electro-resistive heating are identified, and the process is experimentally demonstrated under laboratory conditions. The electric resistive heating of a composite photovoltaic panel, consisting of a solar cell layer (crystalline silicon, c-Si, with a metallic grid), a backsheet, and a glass layer, is analyzed in detail using a virtual model of a single-crystal silicon solar cell implemented as coupled electric-thermal analysis. The temperature dependence of the electrical resistance of the solar cell layer is experimentally measured, and exponential relationships are derived and subsequently incorporated into the numerical model. The virtual model results are validated, demonstrating that, for a given geometry and configuration of the conductive metallic grid (busbars and fingers), the electrical resistance of the semiconductor layer containing the p–n junction governs the temperature achieved during electro-resistive heating as a function of the applied current. Furthermore, results for the terminal current and voltage, current density in the busbars and fingers, electric field intensity, and the resulting temperature within the semiconductor layer of the single-crystal silicon solar cell are presented and analyzed. Full article

Modern photovoltaic technologies are increasingly evaluated not only in terms of power conversion efficiency and cost, but also with respect to resource origin, toxicity, recyclability, and overall life-cycle impacts. Within this broader sustainability framework, bio-based and bio-inspired materials derived from biomass or mimicking biological structures have emerged as promising candidates for a wide range of photovoltaic components, including active layers, interfacial modifiers, substrates, encapsulants, and natural dyes. This review provides a layer-by-layer overview of such materials implemented or proposed in dye-sensitized, organic, perovskite, biohybrid, and silicon solar cells, linking their molecular structures and optoelectronic properties to representative device performances and key degradation pathways. Cross-cutting challenges related to moisture and thermal stability, barrier performance, feedstock variability, and the risk of “greenwashing” are highlighted, emphasizing that sustainability claims must be supported by quantitative metrics such as life-cycle assessment, circularity indicators, and durability studies. Finally, we outline promising research directions in molecular engineering, hybrid biosynthetic architectures, and advanced encapsulation concepts that could enable bio-based materials to make a meaningful contribution to low-impact photovoltaic technologies. Full article

Early identification of thermal and electrical anomalies in grid-connected photovoltaic (PV) systems is becoming increasingly important to reduce energy losses, limit power quality (PQ) degradation, and avoid excessive operating stress on power electronic converters. Conventional electrical monitoring methods can provide overall performance information, but they are generally unable to detect and localize early-stage defects occurring at module or cell level. In this context, the present study proposes an integrated diagnostic framework that combines non-destructive infrared thermography (IRT) with advanced electrical signal processing techniques for PV condition monitoring. The proposed approach correlates thermographic information, capable of revealing defects such as hotspots, cell cracks, and bypass diode failures, with high-frequency electrical signal analysis based on frequency-domain and time–frequency methods, together with deep learning-driven thermographic segmentation. By associating thermal acquisitions with electrical PQ indicators, the framework enables the early detection of physical defects linked to inefficient Maximum Power Point Tracking (MPPT) operation and progressive degradation of PV system performance. The methodology was experimentally validated on a grid-connected photovoltaic installation under different fault conditions, including hotspots, bypass diode anomalies, and localized overheating effects, demonstrating the potential of the proposed approach for predictive maintenance and intelligent PV monitoring applications. The obtained results indicate that the proposed framework improves the reliability of photovoltaic fault detection by combining thermographic inspection with advanced electrical signal analysis and AI-based defect interpretation, thus supporting predictive maintenance strategies in smart PV infrastructures. The proposed approach demonstrates image segmentation capabilities, as evidenced by a precision (PA) of 96.88%, a mean IoU (mIoU) of 77.83% and a macro F1-score of 87.47%. The proposed framework maintained reduced computational requirements compatible with real-time monitoring applications. Full article

Electroluminescence (EL) imaging is widely used for photovoltaic (PV) defect inspection, yet fair comparison of deep learning backbones remains difficult because datasets, labels, and protocols vary across studies. This work presents a controlled image-level benchmark of six architectures (ConvNeXt-T, ViT-B/16, DeiT-B/16, Swin-T, DenseNet121, and MobileNetV3-Large) across five hierarchical tasks for monocrystalline and polycrystalline cells with binary and multi-class labels. A balanced proprietary dataset of 20,000 single-cell EL images was evaluated with identical preprocessing, augmentation, training, and stratified five-fold cross-validation, yielding 150 runs. ConvNeXt-T achieved the highest mean macro-F1 (93.12%) while using about one-third of the parameters of base ViT/DeiT models. On the four-class polycrystalline task, it reached 84.94 ± 0.45% macro-F1, compared with 70.08 ± 1.19% for DenseNet121 and 59.43 ± 1.71% for MobileNetV3-Large. Error analysis revealed conservative missed-defect behavior in lightweight CNNs, especially for surface-level degradation and crack categories. The results provide image-level cross-validation evidence for controlled benchmarking and motivate future module-level grouped validation. Full article

Dye-sensitized solar cells (DSSCs) represent a promising photovoltaic technology for indoor and building-integrated applications due to their colour tunability, semi-transparency, and favourable spectral response. However, the sustainability of conventional devices is hindered by the use of volatile organic solvent-based electrolytes, which raise concerns regarding toxicity, flammability, and long-term stability. This review analyses the evolution of DSSC architecture, with particular focus on electrolyte media, ranging from aqueous systems to deep eutectic solvents and bio-derived quasi-solid architectures. Special attention is focused on the interplay between electrolyte composition, dye design, and interfacial charge-transfer processes. By highlighting recent progress and remaining challenges, this work outlines viable strategies toward safe, durable, and fully sustainable DSSCs tailored for indoor and integrated photovoltaic applications. Full article

Conventional machine learning models for perovskite solar cells predict photovoltaic parameters independently, disregarding the physical constraint $\mathrm{PCE}=V_{oc}\times J_{sc}\times \mathrm{FF}/100$. This approach can yield mutually incompatible predictions for the four parameters, a failure mode that has not been hitherto quantified in the perovskite solar cell literature. This paper proposes a multi-head neural network with a shared backbone, physics-guided feature construction, and task-specific prediction heads, and validates it on 7176 SCAPS-1D simulations across 12 perovskite compositions. When benchmarked against architecturally matched single-task baselines, the multi-task model, optimized via 5-fold cross-validation, achieves $R^2$ values of at least 0.994 for all four targets, with cross-fold standard deviations of 0.001. In particular, fill factor prediction improves from $R^2=0.617\pm 0.254$ (single-task) to $0.994\pm 0.001$ (multi-task), a 233-fold reduction in cross-fold standard deviation. Application of a physical consistency metric developed in this work reveals that 36.5% of single-task predictions exceed a 2 PCE-unit implausibility threshold, compared to only 0.01% for the multi-task model. The multi-task model outperforms the single-task baseline in all 20-fold target comparisons, with large effect sizes (Cohen’s $d=1.33$–$8.93$). These results confirm multi-task learning as an effective approach for achieving robust, stable, and internally consistent predictions in simulation-based photovoltaic virtual screening. Full article

The increasing global demand for renewable energy has intensified the search for high-efficiency and cost-effective solar cell technologies. Quantum dot-sensitized solar cells (QDSSCs) have emerged as promising candidates due to their tunable optoelectronic properties and enhanced light absorption. In this study, SnS quantum dots were synthesized from dithiocarbamate complexes using different ligands, namely m-toluidine (SnS1), aniline (SnS2), and p-toluidine (SnS3), to investigate the influence of precursor chemistry on material properties and device performance. Structural analysis confirmed the formation of an orthorhombic phase for all samples, while morphological studies revealed well-dispersed nanocrystals for SnS1 (5.93 nm), increased aggregation for SnS2 (8.57 nm), and partially fused domains with an intermediate size for SnS3 (6.67 nm). Optical measurements showed bandgap energies of 2.8, 2.2, and 2.7 eV for SnS1, SnS2, and SnS3, respectively, with SnS3 exhibiting reduced charge-recombination behaviour. Photovoltaic devices fabricated using these materials yielded power conversion efficiencies of 3.40, 2.03, and 7.63% for SnS1, SnS2, and SnS3, respectively, with no significant improvement observed for bifacial configurations. The superior performance of SnS3 is attributed to an optimal balance between light absorption, morphology, and charge transport properties, highlighting the critical role of precursor ligand selection in tuning quantum dot characteristics for improved QDSSC performance. Full article

The production of sustainable and cost-effective energy remains a global challenge, with photovoltaic technology emerging as a promising solution. Sensitizers play a key role in electron production in dye-sensitized solar cells, which are emerging photovoltaic devices; thus, different chemical structures have been introduced to achieve the best results. Determining the optimal conditions for the coating and application of dye materials to obtain optimal efficiency and performance is of great importance. For this purpose, an organometallic dye was used to extract the optimal coating conditions. Two factors—ambient temperature during photoanode preparation and anti-aggregation agent concentration—were selected as effective parameters, and the optimal conditions for achieving high efficiency and durability were determined using machine learning. Finally, the findings were analyzed from two perspectives: the preparation of laboratory devices using the selected dye and the evaluation of similar dye materials to validate the proposed optimal conditions. Full article

Hybrid renewable energy systems (HRES) integrating photovoltaic arrays (PV), wind turbines (WT), and fuel cells (FC) require coordinated maximum power extraction to maintain stable operation under dynamic environmental and load conditions. Conventional MPPT approaches based on independent source-level control often suffer from adverse source interaction, increased steady-state oscillation, degraded DC-link stability, and reduced total extracted power when multiple renewable sources operate simultaneously. To address these limitations, this paper proposes an integrated perturb-and-observe control framework for coordinated power optimization in photovoltaic–wind–fuel-cell hybrid renewable energy systems connected through a shared DC-link structure. Unlike conventional independent MPPT controllers, the proposed strategy evaluates the aggregate power behavior of the integrated system and performs coordinated duty-cycle adaptation to improve renewable-energy utilization while suppressing source conflicts and dynamic coupling effects. The proposed controller is implemented and validated using a real-time digital simulator under a sequential disturbance profile consisting of an irradiance drop at 0.2 s, wind-speed increase at 0.4 s, hydrogen-pressure fluctuation at 0.6 s, and load variation at 0.8 s. Comparative evaluation against conventional perturb-and-observe, incremental conductance, and fuzzy-logic-based MPPT methods demonstrates that the proposed framework achieves a tracking efficiency of $97.8\%$, reduces steady-state tracking error to $2.2\%$, and improves settling time by $42.8\%$ under these dynamic operating conditions. In addition, the proposed controller exhibits lower oscillatory behavior, improved extracted renewable power, and enhanced DC-link stability during simultaneous multi-source disturbances. The results demonstrate that the proposed framework provides an effective real-time coordination strategy for hydrogen-enabled hybrid renewable energy systems operating under dynamically coupled renewable-source conditions. Full article