Search Results (1,242)

Copper nitride (Cu₃N) is gaining attention as an eco-friendly thin-film semiconductor in a myriad of applications, including storage devices, microelectronic components, photodetectors, and photovoltaic cells. This work presents a detailed optoelectronic study of Cu₃N thin films grown by reactive RF-magnetron sputtering under pure N₂. An overview of the state-of-the-art literature on this material and its potential applications is also provided. The studied films consist of Cu₃N polycrystals with a cubic anti-ReO₃ type structure exhibiting a preferential (100) orientation. Their optical properties across the UV-Vis-NIR spectral range were investigated using a combination of multi-angle spectroscopic ellipsometry, broadband transmission, and reflection measurements. Our model employs a stratified geometrical approach, primarily to capture the depth-dependent compositional variations of the Cu₃N film while also accounting for surface roughness and the underlying glass substrate. The complex dielectric function of the film material is precisely determined through an advanced dispersion model that combines multiple oscillators. By integrating the Tauc–Lorentz, Gaussian, and Drude models, this approach captures the distinct electronic transitions of this polycrystal. This customized optical model allowed us to accurate extract both the indirect (1.83–1.85 eV) and direct (2.38–2.39 eV) bandgaps. Our multifaceted characterization provides one of the most extensive studies of Cu₃N thin films to date, paving the way for optimized device applications and broader utilization of this promising binary semiconductor, and showing its particular potential for photovoltaic given its adequate bandgap energies for solar applications. Full article

This study investigates the feasibility and possible improvement of microwave power transmission (MPT) in a space-based solar power system (SSPS). SSPS, a concept proposed by Dr. Peter Glaser in 1968, aims to harness solar energy in space, free from atmospheric constraints, and transmit it to Earth using microwaves or lasers. Our focus is on enhancing the efficiency and cost-effectiveness of MPT, which accounts for a significant portion of SSPS expenses. In this work, we propose the use of a novel field emission-based rising-sun magnetron (RM) in the MPT subsystem, aiming to extend system longevity and simplify construction, while dramatically reducing the implementation cost. It is demonstrated that the optimization of the RM design at 2.45 GHz can achieve a high output power of >100 kW with a high efficiency of >85%, based on the well-established conformal finite-difference time-domain particle-in-cell simulations. This research provides valuable insights into improving SSPS, making it a more viable and sustainable renewable energy solution. Full article

Thermoelectric (TE) materials represent a critical frontier in sustainable energy conversion technologies, providing direct thermal-to-electrical energy conversion with solid-state reliability. The optimizations of TE performance demand a nuanced comprehension of structure–property relationships across diverse length scales. This review summarizes established and emerging spectroscopic and microscopic techniques used to characterize inorganic and polymer TE materials, specifically poly(3,4-ethylenedioxythiophene): poly(styrenesulfonate) (PEDOT:PSS). For inorganic TE, ultraviolet–visible (UV–Vis) spectroscopy, energy-dispersive X-ray (EDX) spectroscopy, and X-ray photoelectron spectroscopy (XPS) are widely applied for electronic structure characterization. For phase analysis of inorganic TE materials, Raman spectroscopy (RS), electron energy loss spectroscopy (EELS), and nuclear magnetic resonance (NMR) spectroscopy are utilized. For analyzing the surface morphology and crystalline structure, chemical scanning electron microscopy (SEM), transmission electron microscopy (TEM), and X-ray diffraction (XRD) are commonly used. For polymer TE materials, ultraviolet−visible–near-infrared (UV−Vis−NIR) spectroscopy and ultraviolet photoelectron spectroscopy (UPS) are generally employed for determining electronic structure. For functional group analysis of polymer TE, attenuated total reflectance–Fourier-transform infrared (ATR−FTIR) spectroscopy and RS are broadly utilized. XPS is used for elemental composition analysis of polymer TE. For the surface morphology of polymer TE, atomic force microscopic (AFM) and SEM are applied. Grazing incidence wide-angle X-ray scattering (GIWAXS) and XRD are employed for analyzing the crystalline structures of polymer TE materials. These techniques elucidate electronic, structural, morphological, and chemical properties, aiding in optimizing TE properties like conductivity, thermal stability, and mechanical strength. This review also suggests future research directions, including in situ methods and machine learning-assisted multi-dimensional spectroscopy to enhance TE performance for applications in electronic devices, energy storage, and solar cells. Full article

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

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

Cobalt-doped iron disulfide (FeS₂) thin films were synthesized via chemical bath deposition (CBD) followed by annealing at 450 °C, yielding phase-pure pyrite structures with multifunctional properties. A deposition temperature of 95 °C is critical for promoting Co incorporation, suppressing sulphur vacancies, and achieving structural stabilization of the film. After annealing, the dendritic morphologies transformed into compact quasi-spherical nanoparticles (~100 nm), which enhanced the crystallinity and optoelectronic performance of the films. The films exhibited strong absorption (>50%) in the visible and near-infrared regions and tunable direct bandgaps (1.14 to 0.96 eV, within the optimal range for single-junction solar cells. Electrical characterization revealed a fourth-order increase in conductivity after annealing (up to 4.78 Ω⁻¹ cm⁻¹) and confirmed stable p-type behavior associated with Co²⁺-induced acceptor states and defect passivation. These results demonstrate that CBD enabled the fabrication of Co-doped FeS₂ thin films with synergistic structural, electrical, and optical properties. The integration of earth-abundant elements and tunable electronic properties makes these films promising absorber materials for the next-generation photovoltaic devices. 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

In this study, a novel comparison between single phase 7-Level Packed U—Cell (PUC) inverter and single phase 9-Level Cross Switches Cell (CSC) inverter with Model Predictive Controller (MPC) for solar grid-tied applications is presented. Our innovation introduces a unique approach by integrating PV solar panels in PUC and CSC inverters in their two DC links rather than just one which increases power density of the system. Another key benefit for the proposed models lies in their simplified design, offering improved power quality and reduced complexity relative to traditional configurations. Moreover, both models feature streamlined control architectures that eliminate the need for additional controllers such as PI controllers for grid reference current extraction. Furthermore, the implementation of Maximum Power Point Tracking (MPPT) technology directly optimizes power output from the PV panels, negating the necessity for a DC-DC booster converter during integration. To validate the proposed concept’s performance for both inverters, extensive simulations were conducted using MATLAB/Simulink, assessing both inverters under steady-state conditions as well as various disturbances to evaluate its robustness and dynamic response. Both inverters exhibit robustness against variations in grid voltage, phase shift, and irradiation. By comparing both inverters, results demonstrate that the CSC inverter exhibits superior performance due to its booster feature which relies on generating voltage level greater than the DC input source. This primary advantage makes CSC a booster inverter. 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

Accurate parameter estimation of solar cells is critical for early-stage fault diagnosis in photovoltaic (PV) power systems. A physical model based on three-diode configuration has been recently introduced to improve model accuracy. However, nonlinear and recursive relationships between internal parameters and PV output, along with parameter drift and PV degradation due to long-term operation, pose significant challenges. To address these issues, this study proposes a deep learning-based ensemble framework that integrates outputs from multiple optimization algorithms to improve estimation precision and robustness. The proposed method consists of three stages. First, the collected data were preprocessed using some data processing techniques. Second, a PV power generation system is modeled using the three-diode structure. Third, several optimization algorithms with distinct search behaviors are employed to produce diverse estimations. Finally, a hybrid deep learning model combining convolutional neural networks (CNNs) and long short-term memory (LSTM) networks is used to learn from these results. Experimental validation on a 733 kW PV power generation system demonstrates that the proposed method outperforms individual optimization approaches in terms of prediction accuracy and stability. Full article

This article proposes an Energy Management System (EMS) for smart microgrids with a decentralized multi-agent system (MAS) based on a bio-inspired T-Cell optimization algorithm. The proposed system allows real-time control and dynamic balancing of loads while addressing the challenges of intermittent renewable energy sources like solar and wind. The system operates within the tertiary control layer; the optimal set points are computed by the T-Cell algorithm across energy sources and storage units. The set points are implemented and validated in real-time by the OPAL-RT simulation platform. The system contains a real-time feedback loop, which continuously monitors voltage levels and system performance, allowing the system to readjust in case of anomalies or power imbalances. Contrary to classical methods like Model Predictive Control (MPC) or Particle Swarm Optimization (PSO), the T-Cell algorithm demonstrates greater robustness to uncertainty and better adaptability to dynamic operating conditions. The MAS is implemented over the JADE platform, enabling decentralized coordination, autonomous response to disturbances, and continuous system optimization to ensure stability and reduce reliance on the main grid. The results demonstrate the system’s effectiveness in maintaining the voltages within acceptable limits of regulation (±5%), reducing reliance on the main grid, and optimizing the integration of renewable sources. The real-time closed-loop solution provides a scalable and reliable microgrid energy management solution under real-world constraints. 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

YVO₄ based phosphors have aroused extensive interest in the field of optoelectronics due to their good chemical stability and unique luminescence properties. However, commercialization of YVO₄ phosphors requires high luminescence intensity, enhanced conversion efficiency, and a wide excitation spectrum. In this work, Eu³⁺, Ba²⁺, Bi³⁺ co-doped YVO₄ was prepared by the sol–gel method. The XRD of YVO₄: 5%Eu³⁺, 5%Ba²⁺, 0.5%Bi³⁺ phosphor analysis confirms the pure tetragonal phase, with a fairly large size of approximately 100 nm for the optimal composition. And the SEM and TEM revealed well-dispersed spherical nanoparticles with sizes of 100–120 nm. The introduction of Ba²⁺ ions enhanced the luminescence intensity, while the incorporation of Bi³⁺ ions improved the excitation width of the phosphor. The resulting YVO₄: 5%Eu³⁺, 5%Ba²⁺, 0.5%Bi³⁺ phosphor exhibited a 1.39-times broader excitation bandwidth and a 2.72-times greater luminescence intensity at 618 nm compared to the benchmark YVO₄: 5% Eu³⁺ sample. Additionally, the transmittance of the films in the 350 nm to 800 nm region exceeded 85%. The YVO₄: 5%Eu³⁺, 5%Ba²⁺, 0.5%Bi³⁺ film effectively absorbed ultraviolet light and converted it to red emission, enabling potential applications in solar cell window layers, dye-sensitized cell luminescence layers, and solar cell packaging glass. Full article