Search Results (1,164)

Minimizing surface defects in perovskite films is crucial for suppressing non-radiative recombination and enhancing device performance. Herein, we propose the use of N-bromosuccinimide (NBS), a small molecule containing Lewis base carbonyl groups (C=O), to improve the quality of RbCsMAFA mixed-cation perovskite films. This surface treatment effectively reduces non-radiative charge-carrier recombination, in particular through the passivation of surface defects related to undercoordinated Pb²⁺ ions and halide vacancies, and significantly accelerates charge extraction from the perovskite into the Spiro-OMeTAD hole transporter. Consequently, NBS-treated PerSCs achieve a power conversion efficiency (PCE) of 18.24%, representing an 11% relative increase over the control device (16.48%). This enhancement is mainly attributed to a V_oc gain of up to 40 mV and modifications in the recombination dynamics. Supporting evidence from impedance spectroscopic analyses further confirms enhanced energy-level alignment and reduced interfacial losses, improved charge transport as well as prolonged charge lifetimes within the devices. This work provides a simple yet effective approach to reduce the non-radiative recombination losses towards more efficient and stable PerSCs. Full article

This study explores the use of mid-infrared (MIR) free-electron laser (FEL) irradiation as a tool for tailoring the surface properties of electrochemically synthesized TiO₂—graphene quantum dots (QDs). The QDs, prepared in colloidal form via a cost-effective electrochemical method in a KCl—citric acid medium, were exposed to MIR wavelengths (5.76, 8.02, and 9.10 µm) at the Kyoto University FEL facility. Post-irradiation measurements revealed a pronounced inversion of zeta potential by 40–50 mV and approximately 10% reduction in hydrodynamic size, indicating double-layer contraction and ionic redistribution at the QD—solvent interface. Photoluminescence spectra showed enhanced emission for GQDs and TiO₂/GQD composites, while Tauc analysis revealed modest bandgap blue shifts (0.04–0.08 eV), both consistent with trap-state passivation and sharper band edges. TEM confirmed intact crystalline structures, verifying that FEL-induced modifications were confined to surface chemistry rather than bulk lattice damage. Taken together, these results demonstrate that MIR FEL irradiation provides a resonance-driven, non-contact method to reorganize ions, suppress defect states, and improve the optoelectronic quality of QDs. This approach offers a scalable post-synthetic pathway for enhancing electron transport layers in perovskite solar cells and highlights the broader potential of photonic infrastructure for advanced nanomaterial processing and interface engineering in optoelectronic and energy applications. Full article

Perovskite materials have garnered significant attention in both fundamental research and practical applications owing to their exceptional light absorption coefficients, low fabrication costs, and inherent advantages for thin-film and flexible device fabrication. Nevertheless, interface defects within perovskite films induce detrimental non-radiative carrier recombination and pronounced hysteresis effects, which collectively impose substantial limitations on the photovoltaic performance and long-term operational stability of perovskite solar cells (PSCs). Conventional passivation strategies, despite their demonstrated efficacy in mitigating interface defects, often inadvertently introduce secondary defects in originally defect-free regions, thereby restricting the extent of device performance improvement. To overcome this critical limitation, we have developed a precision defect passivation methodology that employs a targeted two-step immersion–cleaning process, achieving selective defect passivation while concomitantly eliminating residual passivating agents. This approach effectively prevents the formation of new defects in unaffected regions of the perovskite films, and the resultant PSC possesses a power conversion efficiency (PCE) of 21.08%, accompanied by a substantial mitigation of hysteresis behavior. Furthermore, unencapsulated devices demonstrate remarkable stability, retaining over 81% of their initial efficiency after 20 days of atmospheric storage under 50% relative humidity, which underscores the effectiveness of our passivation strategy in simultaneously enhancing both device performance and operational stability. Full article

The increasing use of UAVs has reshaped urban logistics, enabling sustainable alternatives to traditional deliveries. To address critical issues inherent in the system, the proposed study presents the design and evaluation of an innovative unmanned aerial vehicle (UAV) prototype that integrates advanced electronic components and artificial intelligence (AI), with the aim of reducing environmental impact and enabling autonomous navigation in complex urban environments. The UAV platform incorporates brushless DC motors, high-density LiPo batteries and perovskite solar cells to improve energy efficiency and increase flight range. The Deep Q-Network (DQN) allocates energy and selects reference points in the presence of wind and payload disturbances, while an integrated sensor system monitors motor vibration/temperature and charge status to prevent failures. In urban canyon and field scenarios (wind from 0 to 8 m/s; payload from 0.35 to 0.55 kg), the system reduces energy consumption by up to 18%, increases area coverage by 12% for the same charge, and maintains structural safety factors > 1.5 under gust loading. The approach combines sustainable materials, efficient propulsion, and real-time AI-based navigation for energy-conscious flight planning. A hybrid methodology, combining experimental design principles with finite-element-based structural modelling and AI-enhanced monitoring, has been applied to ensure structural health awareness. The study implements proven edge-AI sensor fusion architectures, balancing portability and telemonitoring with an integrated low-power design. The results confirm a reduction in energy consumption and CO₂ emissions compared to traditional delivery vehicles, confirming that the proposed system represents a scalable and intelligent solution for last-mile delivery, contributing to climate resilience and urban sustainability. The findings position the proposed UAV as a scalable reference model for integrating AI-driven navigation and renewable energy systems in sustainable logistics. Full article

All inorganic CsPbBr₃ possesses ideal stability in halide perovskites, but its wide bandgap and relatively poor film quality seriously limit the performance enhancement and possible applications of perovskite solar cells (PSCs). In this work, a triple-functional poly(3-Hexylthiophene) (P3HT) modifier was introduced to realize color-tunable semi-transparent CsPbBr₃ PSCs. From the optical perspective, the P3HT acted as the assistant photoactive layer, enhanced the light absorption capacity of the CsPbBr₃ film, and broadened the spectrum response range of devices. In view of the hole transport layer, P3HT modified the energy level matching between the CsPbBr₃/anode interface and facilitated the hole transport. Simultaneously, the S⁻ in P3HT formed a more stable Pb-S bond with the uncoordinated Pb²⁺ on the surface of CsPbBr₃ and played the role of a defect passivator. As the P3HT concentration increased from 0 to 15 mg/mL, the color of CsPbBr₃ devices gradually changed from light yellow to reddish brown. The PSC treated by an optimal P3HT concentration of 10 mg/mL achieved a champion power conversion efficiency (PCE) of 8.71%, with a V_OC of 1.30 V and a J_SC of 8.54 mA/cm², which are remarkably higher than those of control devices (6.86%, 1.22 V, and 8.21 mA/cm²), as well its non-degrading stability and repeatability. Here, the constructed CsPbBr₃/P3HT heterostructure revealed effective paths for enhancing the photovoltaic performance of CsPbBr₃ PSCs and boosted their semi-transparent applications in building integrated photovoltaics (BIPVs). Full article

We explore a plasmonic interface for perovskite solar cells (PSCs) by integrating inkjet-printed TiO₂-AuNP microdot arrays (MDA) into the electron transport layer. This systematic study examines how the TiO₂ blocking layer (BL) surface conditioning, AuNP layer positioning, and nanoparticle loading collectively influence device performance. Pre-annealing the BL increases its hydrophobicity, yielding smaller and denser AuNP microdots with an enhanced localized surface plasmon resonance (LSPR). Positioning the AuNP MDA at the BL/perovskite interface (above the BL) maximizes near-field plasmonic coupling to the absorber, resulting in higher photocurrent and power conversion devices; these trends are corroborated by finite-difference time-domain (FDTD) simulations. Moreover, these devices demonstrate better stability over time compared to those with AuNPs at the transparent electrode (under BL). Although higher AuNP concentrations improve dispersion stability, preserve MAPI crystallinity, and yield more uniform nanoparticle sizes, device measurements showed no performance gains. After annealing, the samples with the Au content of 23 wt% relative to TiO₂ achieved optimal PSC efficiency by balancing plasmonic enhancement and charge transport without the increased resistance and recombination losses seen at higher loadings. Importantly, X-ray diffraction (XRD) confirms that introducing the TiO₂-AuNP MDA at the interface does not disrupt the perovskite’s crystal structure, underscoring the structural compatibility of this plasmonic enhancement. Overall, our findings highlight a scalable strategy to boost PSC efficiency via engineered light-matter interactions at the nanoscale without compromising the perovskite’s structural integrity. Full article

Perovskite solar cells (PSCs) are next-generation solar cells; they are replacing silicon-based solar cells due to their higher efficiency, greater cost-effectiveness, and enhanced potential for various applications. Exceeding the efficiency of crystalline silicon-based solar cells, the commercialization of PSCs has driven not only the development of perovskite photoactive materials but also charge transport layer advancements, interfacial engineering, and processing technologies. PSCs were developed later than dye-sensitized solar cells and organic solar cells; the adoption of techniques previously employed in these technologies is significant to enhancing their performance. Among them, polymers are widely employed in perovskite solar cells to facilitate efficient charge transport, provide interfacial passivation, enhance mechanical flexibility, enable solution-based processing, and improve environmental stability. In this review, we highlight the roles of polymer materials as charge transport layers, interfacial layers, and other functional layers for highly efficient and stable PSCs. Full article

Perovskites (ABX₃) exhibit remarkable potential in optoelectronic conversion, catalysis, and diverse energy-related fields. However, the tunability of A, B, and X-site compositions renders conventional screening methods labor-intensive and inefficient. This review systematically synthesizes the roles of physical simulations and machine learning (ML) in accelerating perovskite discovery. By harnessing existing experimental datasets and high-throughput computational results, ML models elucidate structure-property relationships and predict performance metrics for solar cells, (photo)electrocatalysts, oxygen carriers, and energy-storage materials, with experimental validation confirming their predictive reliability. While data scarcity and heterogeneity inherently limit ML-based prediction of material property, integrating high-throughput computational methods as external mechanistic constraints—supplementing standardized, large-scale training data and imposing loss penalties—can improve accuracy and efficiency in bandgap prediction and defect engineering. Moreover, although embedding high-throughput simulations into ML architectures remains nascent, physics-embedded approaches (e.g., symmetry-aware networks) show increasing promise for enhancing physical consistency. This dual-driven paradigm, integrating data and physics, provides a versatile framework for perovskite design, achieving both high predictive accuracy and interpretability—key milestones toward a rational design strategy for functional materials discovery. Full article

Binary oxide ceramics have emerged as key materials in solar energy research due to their versatility, chemical stability, and tunable electronic properties. This study presents a comparative analysis of seven prominent oxides (TiO₂, ZnO, Al₂O₃, SiO₂, CeO₂, Fe₂O₃, and WO₃), focusing on their functional roles in silicon, perovskite, dye-sensitized, and thin-film solar cells. A bibliometric analysis covering over 50,000 publications highlights TiO₂ and ZnO as the most widely studied materials, serving as electron transport layers, antireflective coatings, and buffer layers. Al₂O₃ and SiO₂ demonstrate highly specialized applications in surface passivation and interface engineering, while CeO₂ offers UV-blocking capability and Fe₂O₃ shows potential as an absorber material in photoelectrochemical systems. WO₃ is noted for its multifunctionality and suitability for scalable, high-rate processing. Together, these findings suggest that binary oxide ceramics are poised to transition from supporting roles to essential components of stable, efficient, and environmentally safer next-generation solar cells. Full article

The intrinsic structural stability of ABO₃ perovskite materials is a pivotal factor determining their efficiency and durability in photovoltaic applications. However, accurately predicting stability, commonly measured by the energy above hull metric, remains challenging due to the complex interplay of compositional, crystallographic, and electronic features. To address this challenge, we propose a streamlined hybrid machine learning framework that combines the sequence modeling capability of Long Short-Term Memory (LSTM) networks with the robustness of Random Forest regressors. A genetic algorithm-based feature selection strategy is incorporated to identify the most relevant descriptors and reduce noise, thereby enhancing both predictive accuracy and interpretability. Comprehensive evaluations on a curated ABO₃ dataset demonstrate strong performance, achieving an $R^2$ of 0.98 on training data and 0.83 on independent test data, with a Mean Absolute Error (MAE) of 8.78 for training and 21.23 for testing, and Root Mean Squared Error (RMSE) values that further confirm predictive reliability. These results validate the effectiveness of the proposed approach in capturing the multifactorial nature of perovskite stability while ensuring robust generalization. This study highlights a practical and reliable pathway for accelerating the discovery and optimization of stable perovskite materials, contributing to the development of more durable next-generation solar technologies. Full article

Perovskite solar cells (PSCs) have emerged as promising photovoltaic technologies owing to their high power conversion efficiency (PCE) and material versatility. Conventional optimization of PSC architectures largely depends on iterative experimental approaches, which are often labor-intensive and time-consuming. In this study, a data-driven modeling strategy is introduced to accelerate the design of efficient and mechanically robust PSCs. Seven supervised regression models were evaluated for predicting key photovoltaic parameters, including PCE, short-circuit current density (Jsc), open-circuit voltage (Voc), and fill factor (FF). Among these, a stacking ensemble framework exhibited superior predictive accuracy, achieving an R² of 0.8577 and a root mean square error of 2.084 for PCE prediction. Model interpretability was ensured through Shapley Additive exPlanations(SHAP) analysis, which identified precursor solvent composition, A-site cation ratio, and hole-transport-layer additives as the most influential parameters. Guided by these insights, ten device configurations were fabricated, achieving a maximum PCE of 24.9%, in close agreement with model forecasts. Furthermore, multiscale mechanical assessments, including bending, compression, impact resistance, peeling adhesion, and nanoindentation tests, were conducted to evaluate structural reliability. The optimized device demonstrated enhanced interfacial stability and fracture resistance, validating the proposed predictive–experimental framework. This work establishes a comprehensive approach for performance-oriented and reliability-driven PSC design, providing a foundation for scalable and durable photovoltaic technologies. Full article

Perovskite solar cells (PSCs) rarely report, on a single-device platform, concurrent gains in optoelectronic efficiency and buried-interface mechanical robustness—two prerequisites for flexible and roll-to-roll (R2R) integration. We engineered a nanoplate-structured fluorine-doped tin oxide (NP-FTO) front electrode that couples light management with three-dimensional interfacial anchoring, and we quantified both photovoltaic (PV) and nanomechanical metrics on the same device stack. Relative to planar FTO, the NP-FTO PSCs achieved PCE of up to 25.65%, with simultaneous improvements in Voc (to 1.196 V), Jsc (up to 26.35 mA cm⁻²), and FF (to 82.65%). Nanoindentation revealed a ~28% increase in reduced modulus and >70% higher hardness, accompanied by a ~32% reduction in maximum indentation depth, indicating enhanced load-bearing capacity consistent with the observed FF gains. The low-temperature, solution-compatible NP-FTO interface is amenable to R2R manufacturing and flexible substrates, offering a unified route to bridge high PCE with reinforced interfacial mechanics toward integration-ready perovskite modules. Full article

A novel, eco-friendly perovskite solar cell design is investigated using numerical simulations based on the finite-difference time-domain (FDTD) method. The proposed structure incorporates a two-dimensional (2D) photonic crystal (PhC) architecture featuring a titanium dioxide (TiO₂) cylindrical electron extraction layer. To reduce fabrication complexity and overall production costs, a hole-transport-layer-free (HTL-free) configuration is employed. Simulation results reveal a significant enhancement in photovoltaic performance compared to conventional planar structures, achieving an ultimate efficiency of 42.3%, compared to 36.6% for the traditional design—an improvement of over 16%. Electromagnetic field distributions are analyzed to elucidate the physical mechanisms behind the enhanced absorption. The improved optical performance is attributed to strong coupling between photonic modes and surface plasmon polaritons (SPPs), which enhances light–matter interaction. Furthermore, the device exhibits polarization-insensitive and angle-independent absorption characteristics, maintaining high performance for both transverse magnetic (TM) and transverse electric (TE) polarizations at incidence angles up to 60°. These findings highlight a promising pathway toward the development of cost-effective, lead-free perovskite solar cells with high efficiency and simplified fabrication processes. Full article

This study presents a machine-learning framework for predicting spectral solar irradiance from 300 to 1100 nm using the random forest and neural network models. Two approaches were compared: one using the real aerosol optical depth at discrete wavelengths and the other using the synthetic aerosol optical depth derived from the Ångström exponent. The models were trained on atmospheric variables and were validated against high-resolution spectroradiometer data from the Korea Institute of Energy Research. The models based on the synthetic aerosol optical depth outperformed real-data models in terms of accuracy, bias, and variance explained (γ² = 0.979 vs. 0.967). These models also provide more reliable estimates of the spectral mismatch factor across the diverse photovoltaic technologies. The copper indium gallium diselenide and mono-crystalline silicon cells showed a high spectral mismatch factor stability, whereas Perovskite cells exhibited a greater sensitivity to spectral variations. Full article

Lead-free perovskite solar cells (PSCs) provide a viable alternative to lead-based versions, thereby reducing significant environmental issues related to toxicity. MASnIBr₂ has emerged as a very attractive lead-free perovskite material due to its environmentally friendly characteristics and advantageous optoelectronic capabilities. However, more tuning is required to achieve superior conversion efficiencies (PCEs). This study uses SCAPS-1D simulations to systematically develop and optimize the electron and hole transport layers (ETLs/HTLs) in MASnIBr₂-based perovskite solar cells (PSCs). Iterative simulations are used to carefully examine and optimize critical parameters, including electron affinity, energy bandgap, layer thickness, and doping density. Additionally, the thickness of the MASnIBr₂ absorber layer is optimized to enhance charge extraction and light absorption. Our findings showed a maximum power conversion efficiency of 20.42%, an open-circuit voltage of 1.38 V, a short-circuit current density of 17.91 mA/cm², and a fill factor of 82.75%. This study establishes a basis for future progress in sustainable photovoltaics and offers essential insights into the design of efficient lead-free perovskite solar cells. Full article