Search Results (2,984)

As interest in PV installation has spiked in recent years, the need for optimizing several factors of PV performance has become crucial. These are tilt angle and solar cell temperature (taking into account ambient temperature) and their effect on solar insolation for solar photovoltaic (PV) systems. The objective of this study is to achieve the optimal tilt angle and cell temperature accordingly by developing a MATLAB program to reach the target of maximizing the received solar insolation. To achieve this, additional solar angles such as the azimuth, hour, latitude angle, declination angle, hour angle, and azimuth angle need to be calculated. By computing the solar insolation for specific regions of interest, specifically the Gulf Cooperation Council (GCC) countries, the desired results can be obtained. Additionally, the study aims to assess the influence of PV cell temperature on the I–V curves of commercially available PV modules, which will provide insights into the impact of temperature on the performance characteristics of PV cells. By employing a developed model, the study examined the combined collective influences of solar received radiation, tilt angle, and ambient temperature on the output power of PV systems in five different cities. The annual optimal tilt angles were found to be as follows: Mecca (21.4° N)—21.48°, Fujairah (25.13° N)—25.21°, Kuwait (29.3° N)—29.38°, Baghdad (33.3° N)—33.38°, and Mostaganem (35.9° N)—2535.98°. Notably, the estimated yearly optimal tilt angles closely corresponded to the latitudes of the respective cities. Additionally, the study explored the impact of ambient temperature on PV module performance. It was observed that an increase in ambient temperature resulted in a corresponding rise in the temperature of the PV cells, indicating the significant influence of environmental temperature on PV module efficiency. Overall, the findings demonstrate that adjusting the tilt angle of PV modules on a monthly basis led to higher solar power output compared to yearly adjustments. These results underscore the importance of considering both solar radiation and ambient temperature when optimizing PV power generation. 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

The aim of this paper is to present a novel and comprehensive methodology for the dynamic modeling and experimental validation of a photovoltaic/thermal system. The dynamic model is divided into thermal and electrical subsystems, encompassing the photovoltaic/thermal module and the thermal energy storage. The thermal subsystem of both the photovoltaic/thermal module and the thermal energy storage is described by a one-dimensional dynamic model of heat transfer mechanisms and optical losses, while the electrical subsystem is presented as an electrical equivalent circuit of double diode solar cell. Model validation was conducted on a modern experimental photovoltaic/thermal system over an extended operational period at a five-minute resolution, with validation days classified as sunny, cloudy, or overcast based on weather conditions, thereby demonstrating an applied approach. The results demonstrate the lowest deviation values reported to date, confirmed using six quantitative indicators. The added value of the proposed methodology, not previously addressed in the literature, lies in the following contributions: (i) comprehensive modeling of the entire photovoltaic/thermal system, (ii) accurate consideration of optical losses in the photovoltaic/thermal module, and (iii) long-term experimental validation. Overall, the proposed methodology provides a reliable and efficient framework for PV/T system design, optimization, and long-term performance assessment. 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

Filtration of submicron particles and nanoparticles is an important problem in nano-industry and in air conditioning and ventilation systems. The presence of submicron particles comprising fungal spores, bacteria, viruses, microplastic, and tobacco-smoke tar in ambient air is a severe problem in air conditioning systems. Many nanotechnology material processes used for catalyst, solar cells, gas sensors, energy storage devices, anti-corrosion and hydrophobic surface coating, optical glasses, ceramics, nanocomposite membranes, textiles, and cosmetics production also generate various types of nanoparticles, which can retain in a conveying gas released into the atmosphere. Particles in this size range are particularly difficult to remove from the air by conventional methods, e.g., electrostatic precipitators, conventional filters, or cyclones. For these reasons, nanofibrous filters produced by electrospinning were developed to remove fine particles from the post-processing gases. The physical basis of electrospinning used for nanofilters production is an employment of electrical forces to create a tangential stress on the surface of a viscous liquid jet, usually a polymer solution, flowing out from a capillary nozzle. The paper presents results for investigation of the filtration process of metal oxide nanoparticles: TiO₂, MgO, and Al₂O₃ by electrospun nanofibrous filter. The filter was produced from polyvinylidene fluoride (PVDF). The concentration of polymer dissolved in dimethylacetamide (DMAC) and acetone mixture was 15 wt.%. The flow rate of polymer solution was 1 mL/h. The nanoparticle aerosol was produced by the atomization of a suspension of these nanoparticles in a solvent (methanol) using an aerosol generator. The experimental results presented in this paper show that nanofilters made of PVDF with surface density of 13 g/m² have a high filtration efficiency for nano- and microparticles, larger than 90%. The gas flow rate through the channel was set to 960 and 670 l/min. The novelty of this paper was the investigation of air filtration from various types of nanoparticles produced by different nanotechnology processes by nanofibrous filters and studies of the morphology of nanoparticle deposited onto the nanofibers. Full article

This study presents a computational investigation into the design of triphenylamine-based donor chromophores incorporating 2-(1,1-dicyanomethylene)rhodanine as the acceptor unit. Three molecular architectures (System-1 to System-3) were developed by introducing distinct thiophene-derived $\pi$-bridges to modulate their electronic and optical characteristics for potential application in bulk heterojunction organic solar cells (OSCs). Geometrical optimizations were performed at the B3LYP/6-31+G(d,p) level, while excited-state and absorption properties were evaluated using TD-DFT with the CAM-B3LYP functional. Frontier orbital analysis revealed efficient charge transfer from donor to acceptor moieties, with System-3 showing the narrowest HOMO–LUMO gap (1.96 eV) and the lowest excitation energy (2.968 eV). Charge transport properties, estimated from reorganization energies, indicated that System-2 exhibited the most favorable balance for ambipolar transport, featuring the lowest electron reorganization energy (0.317 eV) and competitive hole mobility. Photovoltaic parameters calculated with PC₆₁BM as acceptor predicted superior $V_{oc}$, $J_{sc}$, and fill factor values for System-2, resulting in the highest theoretical power conversion efficiency (10.95%). These findings suggest that $\pi$-bridge engineering in triphenylamine-based systems can significantly enhance optoelectronic performance, offering promising donor materials for next-generation OSC devices. Full article

This paper presents a broadband omnidirectional rectenna combined with a solar cell for hybrid energy harvesting, addressing the daytime-only limitation of solar cells via complementary RF energy harvesting. To avoid mutual interaction in integration, the solar cell is placed above the antenna to receive light/EM waves from different directions. A broadband discone antenna ensures omnidirectional RF reception from 1.56 to 6.63 GHz, while a single-stub matching circuit and voltage doubler enable rectifier operation from 1.4 to 3.6 GHz, with over 50% power conversion efficiency at 5 dBm. The measurement demonstrates that the hybrid system can yield 20.25 mW from combined RF/solar power. This broadband hybrid energy harvesting system shows potential for powering sensors throughout the day by integrating two complementary energy sources with minimal interaction. Full article

This study presents the development and validation of a high-efficiency optical interface designed for ultra-high-concentration photovoltaic (UHCPV) systems, with a focus on enabling clean and sustainable solar energy conversion. A Fresnel lens serves as the primary optical concentrator in a novel system architecture that integrates advanced optical design with system-level thermal management. The proposed modeling framework combines detailed 3D ray tracing with coupled thermal simulations to accurately predict key performance metrics, including optical concentration ratios, thermal loads, and component temperature distributions. Validation against theoretical and experimental benchmarks demonstrates high predictive accuracies within 1% for optical efficiency and 2.18% for thermal performance. The results identify critical thermal thresholds for long-term operational stability, such as limiting mirror temperatures to below 52 °C and photovoltaic cell temperatures to below 130 °C. The model achieves up to 89.08% optical efficiency, with concentration ratios ranging from 240 to 600 suns and corresponding focal spot temperatures between 37.2 °C and 61.7 °C. Experimental benchmarking confirmed reliable performance, with the measured results closely matching the simulations. These findings highlight the originality of the coupled optical–thermal approach and its applicability to concentrated photovoltaic design and deployment. This integrated design and analysis approach supports the development of scalable, clean photovoltaic technologies and provides actionable insights for real-world deployment of UHCPV systems with minimal environmental impact. 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

It is well known that temperature strongly affects the photovoltaic (PV) performance. Raising the working temperature leads to a significant decrease in PV output of the power capacity, and it also lowers power conversion efficiency. This issue is highly important for the PV systems operating in tropical climate areas such as southern Viet Nam. Developing the cooling methods applied for reducing the PV module temperature might be the solution to this problem and has attracted many researchers and industrial sectors. However, the existing research might not sufficiently address the comparative evaluation of multiple active water-based cooling methods on power conservation efficiency, power output, and cost implications under high-temperature conditions in tropical areas. This study is a case study that aims at conducting some experimental investigations for active water-based cooling methods applied to PV modules in Ho Chi Minh City, South Viet Nam. There are four active water-based cooling methods, including the spraying liquid method (SL), the dripping droplet method (DD), tube heat exchanger method (TE), and the liquid flowing on the PV surface method (LF), that have been developed and experimentally investigated. The voltage, current, temperature, and humidity of the PV cells have been automatically recorded in every one-minute interval via sensors and electronic devices. The experimental results indicate that the surface temperature, the power conversion efficiency, and the output power of PV module are developed toward the useful and positive direction with four cooling methods. In detail, the SL is the best one, in which it leads the PV temperature to reduce from 52 °C to 34–35 °C, the output power increases up to 6.3%, its power conversion efficiency improves up to 2%, while the water flow rate is at its lowest with 0.65 L/min. Similarly, LF also creates results that are similar to SL, but it needs a higher amount of cooling water, which is up to 3.27 L/min. The other methods, like DD and TE, have less power conversion efficiency compared to the SL; it improves only around 1 to 1.3%. These results might be useful for improving the benefits of PV power generation in some tropical regions and contributing to the green energy development in the world. 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

The dye-sensitized solar cell (DSSC) is a promising candidate, offering an attractive substitute for conventional silicon-based photovoltaic technologies. The performance advantages of the DSSC have led to a surge in research activity reflected in the number of publications over the years. To deliver data-driven analysis of DSSC performance, machine learning models have been applied. As a first step, a literature-based database has been developed and after the data preprocesses, Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), xgboost (XGB), and Artificial Neural Network (ANN) algorithms were applied with stratified train-test splits. The performance of the models has been assessed via metrics, and the model interpretability relied on SHAP analysis. Based on the employed metrics and the confusion matrix, DT, RF, and KNN are the most accurate models for predicting DSSC efficiency on the developed dataset. Furthermore, it was revealed that synthesis temperature and the thickness of thin film were identified as the dominant drivers, followed by precursor and dye. Mid-tier contributors were morphological structure, electrolyte concentrations, and the absorption maximum. The results suggest that in optimizing the manufacturing process, targeted tuning of the synthesis temperature, the thickness of thin film, the precursor, and the dye are likely to improve the performance of the device. Therefore, experimental effort should concentrate on these factors. Full article