Search Results (1,233)

The rapid advancement of photovoltaic (PV) technologies has transformed solar energy systems into intelligent, high-efficiency platforms. This review systematically examines next-generation PV materials, hybrid system architectures, and intelligent control strategies. Key technologies include perovskite-based tandem cells, N-type TOPCon, bifacial, heterojunction (HJT), and photovoltaic-thermal (PVT) systems. These innovations overcome the intrinsic limitations of conventional P-type silicon panels by reducing recombination losses, mitigating light- and temperature-induced degradation, and enhancing energy yield under real-world operating conditions. At the system level, AI-enabled inverters, adaptive maximum power point tracking (MPPT), predictive maintenance, and real-time grid interaction enable dynamic optimization under variable irradiance, thermal stress, and load fluctuations. A critical comparison across diverse deployment environments highlights current challenges, including manufacturing complexity, material stability, and AI data-quality limitations. Despite higher upfront costs and system complexity, these advanced PV systems offer superior long-term performance, improved reliability, and reduced levelized cost of electricity through lower degradation rates and enhanced operational resilience. Collectively, intelligent, material-optimized PV technologies represent a scalable, sustainable, and grid-compatible solution for solar energy deployment across diverse climates, supporting the global transition toward low-carbon energy infrastructures. Full article

Lead-free halide double perovskites have emerged as promising candidates for sustainable optoelectronic and thermoelectric applications due to their tunable band gaps, high stability, and non-toxic nature. In this work, we systematically investigate the structural, electronic, optical, and thermoelectric properties of novel double perovskite compounds Rb₂InCuX₆ (X = F, Cl, Br) using density functional theory (DFT) combined with spin–orbit coupling (SOC). The structural stability of these materials is confirmed by evaluating the tolerance factor, octahedral factor, and negative formation energy. Accurate band structures obtained via the modified Becke–Johnson (mBJ) potential and SOC reveal direct band gaps of 1.49 eV, 0.91 eV, and 0.56 eV for Rb₂InCuX₆ (X = F, Cl, Br), indicating their suitability for solar cell applications. Optical properties, derived from the dielectric functions calculated within the Kramers–Kronig framework over a photon energy range up to 14 eV, show strong absorption peaks in the ultraviolet region, making these materials attractive for high-frequency optical conversion devices. Furthermore, thermoelectric parameters, including the Seebeck coefficient, electrical conductivity, electronic thermal conductivity, and power factor, are computed using the BoltzTraP code. Notably, the figure of merit (ZT) approaches 0.80 for Rb₂InCuF₆, close to the ideal value of unity, demonstrating excellent thermoelectric performance over a wide temperature range (200–800 K). Our findings establish Rb₂InCuX₆ (X = F, Cl, Br) as promising lead-free double perovskites for integrated optoelectronic and thermoelectric applications. Full article

Halide double perovskite materials have been used for various applications; their bandgap (E_g) and heat of formation (ΔH_f) are their key properties. They can be obtained through calculations based on high-throughput density functional theory (DFT), but such calculations are computationally expensive and time-consuming. Machine learning (ML) has proved to be an effective tool for screening potential materials. The prediction accuracy of ML models strongly depends on both input features and ML algorithms. However, there is no unified feature set with which ML models can effectively distinguish halide double perovskite materials. Although it has been proven that stacking ML models can achieve higher prediction accuracy than individual ML models, little attention has been paid to the optimization of stacking models. To solve these problems, we constructed a new feature set obtained from periodic tables for predicting the E_g and ΔH_f of halide double perovskites, and we further proposed a method integrating the nondominated sorting genetic algorithm (NSGA-II) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) decision-making tool for stacking model optimization to predict the E_g and ΔH_f of 540 compounds of halide double perovskites. Experimental results from 40 runs of 5-fold cross-validation demonstrate that our proposed new feature set enables ML models to achieve better performance than the original feature set. Moreover, the stacking model optimized by our proposed method yields better predicting performance than that of any individual single model and stacking regression models without optimization, with average improvements of 5.02%, 2.70%, 3.72% and 0.28% in MSE, RMSE, MAE and R², respectively, in E_g prediction, thus providing more effective guidance for screening potential compounds for solar cells from a large quantity of materials. Full article

Lead-free perovskite solar cells have become promising materials in the solar energy field; however, there are some constraints limiting their efficiency, like unfavorable band alignment, high defect densities, and inefficient charge extraction. Cs₃Sb₂I₉ is a lead-free material that has excellent stability, but its experimentally reported efficiencies remain low (<4%). Therefore, Cs₃Sb₂I₉ device performance was investigated using the one-dimensional Solar Cell Capacitance Simulator (SCAPS-1D), where the planar n–i–p structure was analyzed, focusing on its band alignment, transport layers, and key device parameters. The optimized device achieved a power conversion efficiency (PCE) of 13.62%, an open circuit voltage (Voc) of 1.37 V, a short circuit current density (Jsc) of 11.77 mA/cm², and a fill factor (FF) of 84.15% with a 180 nm PCBM electron transport layer, a 150 nm Cu₂O hole transport layer, and a 500 nm absorber thickness. This study advances the development of efficient lead-free perovskite solar cells, promoting sustainable and clean energy. Full article

Space photovoltaics remains the primary power source for satellites and spacecraft, where high efficiency, radiation resistance, and low mass are essential requirements. While conventional III–V multijunction solar cells currently represent the technological benchmark, recent advances in materials science and device architectures have significantly expanded the design space of space photovoltaic systems. This review provides a comprehensive overview of the fundamental physical principles, material platforms, and device concepts relevant to photovoltaic operation under space conditions, with particular emphasis on the AM0 spectrum, radiation effects, and thermal cycling. Special attention is devoted to advanced architectures, including inverted metamorphic multijunction solar cells, concentrator photovoltaic systems, and emerging tandem concepts such as perovskite/silicon and all-perovskite devices. The review highlights the growing importance of system-level metrics, particularly specific power and integration flexibility, which increasingly complement efficiency as key performance indicators. Although emerging technologies offer unprecedented opportunities for lightweight and high-efficiency photovoltaic systems, challenges related to long-term stability, defect control, and scalability remain critical for their practical implementation. Overall, the future of space photovoltaics lies in the development of application-specific solutions that balance efficiency, durability, mass, and cost, enabling next-generation space missions and energy systems. Full article

Narrow-bandgap tin–lead (Sn–Pb) perovskite solar cells (PSCs) are essential for high-performance tandem photovoltaics, yet their operational stability and efficiency suffer from spontaneous Sn²⁺ oxidation, interfacial defects, and non-radiative recombination. Current passivation strategies often provide only a single modification mode and struggle to adequately stabilize Sn²⁺ without introducing charge-transport barriers. Here, we introduce morpholine acetate (MPAC) as a novel interfacial passivator to achieve synergistic chemical and field-effect passivation in Sn–Pb perovskites. The acetate group of MPAC coordinates with undercoordinated metal cations, suppressing Sn²⁺ oxidation and minimizing defect states. Simultaneously, the morpholine moiety forms an interfacial dipole layer that aligns energy levels to facilitate charge extraction. Consequently, MPAC-modified PSCs achieve a champion power conversion efficiency of 22.64%. Under continuous AM 1.5G illumination without optical filters (xenon lamp, 65 °C, open-circuit conditions), the unencapsulated devices maintain over 90% of their initial efficiency after 192 h, providing a promising route to balance performance and durability. Full article

Perovskite solar cells in aerospace applications are promising due to their high power output, radiation tolerance, and ability to extend spacecraft operational lifetimes. Numerical modelling is widely used to optimize solar cells as it can predict the real-world behavior of a device. In this work, we present a numerical simulation of CsMAFA-based perovskite solar cells with monolayer graphene as the front electrode. The model is implemented in the COMSOL Multiphysics^® finite-element environment. Graphene is modelled using the Kubo formula to account for its frequency-dependent surface conductivity, and the electromagnetic wavs interface is coupled with the semiconductor module to capture optical–electrical interactions. The influence of absorber layer thickness on the current density is also examined by sweeping the perovskite absorber thickness (300–450 nm). The current voltage characteristic demonstrates higher current density (27 mA/cm²) at an absorber thickness of ~450 nm. Shockley–Read–Hall recombination (SRH) is studied inside the model and maximum recombination was found to be centred in the absorber layer. The graphene/HTL side shows an SRH recombination of 2 × 10²⁰ cm⁻³ s⁻¹, which is much lower than what is typically seen at ITO-based HTL interfaces. Full article

The commercialization of perovskite solar cells (PSCs) hinges on replacing toxic lead-based absorbers with environmentally benign alternatives while maintaining competitive power conversion efficiencies (PCE). However, the enormous parameter space governing lead-free device architectures—spanning absorber thickness, defect density, doping concentration, and charge transport layer (CTL) selection—renders traditional trial-and-error optimization impractical. This paper introduces PerovskiteOpt-AI, a machine learning (ML)-driven multi-parameter optimization framework that integrates SCAPS-1D device simulation with Gaussian process (GP) surrogate modeling and Bayesian optimization (BO) to systematically identify high-efficiency lead-free PSC configurations. A synthetic dataset of 12,000 device-level simulations generated for the FTO/WS₂/CsSnI₃/CuSCN/Au architecture by varying eight critical parameters. An ensemble of ML models—random forest (RF), XGBoost, and GP regression (GPR)—is trained and benchmarked, with XGBoost achieving an ${\mathrm{R}}^2$ of 0.9987 and RMSE of 0.041% for PCE prediction. The GP surrogate is then coupled with a BO loop employing expected improvement (EI) acquisition to navigate the design space, converging on an optimized PCE of 27.83% ± 0.21% within 150 iterations—a 38.6% relative improvement over the baseline. Shapley additive explanations (SHAP) analysis reveals that absorber defect density and perovskite thickness are the dominant efficiency drivers, while conduction band offset at the ETL/absorber interface governs open-circuit voltage. The proposed framework reduces the computational cost of full-factorial parametric sweeps by over 95%, establishing a scalable paradigm for accelerated, interpretable design of next-generation lead-free consumer-grade photovoltaic devices. Full article

This paper investigates the stability of perovskite films under bonding conditions, focusing on the impact of bonding temperature on the electrical, morphological, and elemental characteristics of perovskite solar cells (PSCs) incorporating a barium–strontium titanate (BST) barrier layer. This study aimed to elucidate the interdiffusion phenomena at interfaces and their effect on device performance. We found that increasing the bonding temperature significantly degrades PSC performance, with efficiencies dropping from 21% at 100 °C to 65% at 180 °C relative to unbonded devices. A critical bonding temperature of 150 °C was identified, which correlates with a pronounced drop in short-circuit current and a peak in series resistance, phenomena primarily attributed to severe elemental interdiffusion and defect formation at the interfaces. Morphological (SEM) and elemental (EDS) analyses confirmed the temperature-dependent nature of interdiffusion across the Au/BST/perovskite interfaces. These findings underscore the critical role of bonding temperature in triggering interfacial degradation, a factor that mediates the stability of BST-interfaced PSCs during packaging. Full article

The ambient stability of ambient-processed lead-free perovskite absorbers remains a critical challenge toward scalable, eco-friendly photovoltaics. Herein, we systematically investigate the time-dependent structural and morphological evolution of drop-cast ambient-processed Cs₃Sb₂I₉ thin films, being a potential non-toxic and stable solar absorber candidate (energy bandgap ~2 eV) for solar cells, stored under uncontrolled ambient condition (~60% Relative humidity) for 28 days. Sequential X-ray diffraction (XRD) and surface morphology analyses using scanning electron microscope (SEM) reveal that the films preserve their trigonal P$\overline{3}$m1 phase throughout aging, confirming phase stability. Moderate moisture exposure may induce partial recrystallization and subtle structural reorganization, possibly including minor c-axis realignment, leading to reduced lattice strain and improved crystallite coherence. Even after prolonged aging, no secondary phases or micro-cracks are detected, underscoring the slow degradation kinetics and robust Sb–I bonding that stabilize the layered [Sb₂I₉]³⁻ dimers. The late-stage increase in diffraction intensity and partial recovery of crystallographic parameters could indicate transient structural reorganization, potentially associated with moisture-mediated reordering within an overall degradation pathway. These observations suggest some degree of morphological persistence and structural tolerance of Cs₃Sb₂I₉ under ambient conditions, rather than complete stability. This behavior offers useful insights into ambient processing and the long-term reliability of lead-free perovskite photovoltaics. Full article

Crystalline silicon solar cells have long dominated the global photovoltaic market due to their mature manufacturing processes, excellent stability, and abundant raw material reserves, accounting for over 90% of the total PV market share. However, single−junction c−Si solar cells are approaching the Shockley–Queisser (SQ) efficiency limit of ~29.4%, creating an urgent need for next−generation PV technologies to achieve higher power conversion efficiency (PCE). Monolithic perovskite/silicon tandem solar cells (PSTSCs) stand as the most commercially promising technology to surpass the single−junction efficiency limit. Since their first demonstration in 2015, PSTSCs have experienced rapid technological advancement, with the certified PCE reaching 35.0% in 2026. This review posits that their rapid efficiency ascent is not serendipitous but driven by synergistic innovations across critical subsystems. We systematically deconstruct these efficiency drivers, encompassing top−cell materials, bottom−cell architecture, and optical management. We conclude by outlining future research frontiers essential for transforming this lab−champion technology into a mainstream energy solution. Full article

This research study conducts a computational analysis of a two-terminal (2T) Perovskite-on-silicon (PVK-Si) solar cell with a tandem configuration. The motivation for this analysis arises from the outstanding potential of PVK-Si solar cells to surpass the efficiency limitations of conventional photovoltaic technology. The tandem configuration utilizes a combination of CsSnBr₃ in the top sub-cell and crystalline silicon (c-Si) in the bottom sub-cell. After optimizing parameters of the top sub-cell (FTO/TiO₂/CsSnBr₃/rGO/Au), which included the thicknesses of CsSnBr₃ (500 nm), TiO₂ (40 nm), rGO (50 nm), the interface defects (10¹³ cm⁻²), and the bandgap of CsSnBr₃ (1.78 eV), the PVK-Si tandem device was simulated. As a result, the top CsSnBr₃ sub-cell achieved an efficiency of 21.62%, while the bottom silicon sub-cell achieved an efficiency of 23.48%. Subsequently, the sub-cells were interconnected in series using filtered spectra and current-density matching. After interpolating the J-V curves, the tandem exhibited a global efficiency of 29.76%, a fill factor (FF) of 85.30%, a matched current density (J_SC) of 19.02 mA/cm², and an open-circuit voltage (V_OC) of 1.83 V. The EQE results confirmed efficient photon management via complementary sub-cell absorption. The performance is competitive with experimental lead-based tandems and exceeds that of current lead-free simulations. Therefore, this research proposes a viable pathway for the development of non-toxic, cost-effective tandem solar systems with manufacturing capabilities. Full article

Photovoltaic technologies represent an increasingly relevant alternative for developing renewable energy sources, particularly those based on light-harvesting materials such as perovskite solar cells (PSCs) and dye-sensitized solar cells (DSSCs), which have achieved efficiencies of 27.3% and 13.0%, respectively. In this context, phenothiazine (PTZ) has attracted considerable interest as a structural block due to its outstanding structural and photophysical properties, which also represent low production costs and reduced environmental impact. This review presents recent advances in the design and development of phenothiazine-based organic materials for photovoltaic applications, analyzing the main synthetic routes for obtaining this nucleus, as well as the fundamental aspects related to the operation of solar cells, including relevant device parameters. Furthermore, several studies focused on the synthesis, characterization, and performance of new phenothiazine-derived molecules used in photovoltaic devices are also examined. Finally, the most relevant conclusions are discussed, and future perspectives for the use of these materials in solar technologies are proposed. Full article

This work proposes the doping of bi-electron transport layers consisting of TiO₂/SnO₂ with iron to facilitate electron movement and recombination reduction, which results in increases in power conversion efficiency and stability enhancement. Two different PSC structures are used: device 1—FTO/TiO₂/SnO₂/MAPbI₃/Spiro-OMETAD/Ag; device 2, a modified device—FTO/TiO₂/SnO₂ + Fe/MAPbI₃/Spiro-OMETAD/Ag. Characterization analysis revealed an improvement in perovskite crystallinity in the modified device; this leads to reductions in trap state density and the recombination of charges that enhance charge extraction. UV-vis absorbance enhancement in the modified device revealed an enhancement in the perovskite layer morphology and good coverage. As a result, PSCs with a short circuit current of 23.35 mA/cm², open circuit voltage of 1.07 V, fill factor of 0.73, and high PCE of 18.17% are obtained from device 2, compared to PSCs with only 22.13 mA/cm², 1.03 V, 0.7, and 16.053% for device 1 without Fe doping, respectively. The results reveal that the device based on Fe doping is more stable than the pristine one under stability tests with regard to aging, thermal, stress and prolonged light. Full article

Nanophotonics, an interdisciplinary field merging nanotechnology and photonics, has enabled transformative advancements across diverse sectors, including green energy, biomedicine, and optical computing. This review comprehensively examines recent progress in nanophotonic principles and applications, highlighting key innovations in material design, device engineering, and system integration. In renewable energy, nanophotonics allows the use of light-trapping nanostructures and spectral control in perovskite solar cells, concentrating solar power systems, and thermophotovoltaics. This has significantly enhanced solar conversion efficiencies, approaching theoretical limits. In biosensing, nanophotonic platforms achieve unprecedented sensitivity in detecting biomolecules, pathogens, and pollutants, enabling real-time diagnostics and environmental monitoring. Medical applications leverage tailored light–matter interactions for precision photothermal therapy, image-guided surgery, and early disease detection. Furthermore, nanophotonics underpins next-generation optical neural networks and neuromorphic computing, offering ultrafast, energy-efficient alternatives to von Neumann architectures. Despite rapid growth, challenges in scalability, fabrication costs, and material stability persist. Future advancements will rely on novel materials, AI-driven design optimization, and multidisciplinary approaches to enable scalable, low-cost deployment. This review summarizes recent progress and highlights future trends, including novel material systems, multidisciplinary approaches, and enhanced computational capabilities, paving the way for transformative applications in this rapidly evolving field. Full article