A ternary CdS–MoS₂–rGO photocathode was developed to enhance visible light-driven hydrogen evolution through interfacial heterostructure engineering. The composite was fabricated via a solution-based deposition method followed by thermal conversion, resulting in crystalline CdS and MoS₂ phases that were uniformly integrated within a conductive reduced graphene oxide (rGO) framework. Structural and surface analyses (XRD and XPS) confirmed the coexistence of Cd²⁺, Mo⁴⁺, and S²⁻ chemical states without detectable secondary phases. Photoelectrochemical measurements revealed that the ternary architecture significantly improves charge separation efficiency and interfacial charge-transfer kinetics compared to binary and single-component films. The CdS–MoS₂–rGO photocathode exhibited the highest photocurrent density, reduced charge-transfer resistance, and favorable Tafel slope under visible-light irradiation (0.25 sun, neutral electrolyte). Gas chromatography measurements verified that these electrochemical enhancements translate into increased hydrogen production rates, following the trend: CdS–MoS₂–rGO > CdS–rGO > MoS₂–rGO >> rGO. Applied bias photon-to-current efficiency (ABPE) analysis further confirmed improved photon utilization efficiency in the ternary system. The enhanced performance is attributed to synergistic integration of CdS (light harvesting), rGO (rapid electron transport), and MoS₂ (catalytic edge sites), which suppresses recombination and accelerates proton reduction kinetics. These findings demonstrate that rational multi-component heterostructure design is an effective strategy for improving hydrogen evolution rate under mild operating conditions.

Nano porous silicon was achieved by electrochemical etching technique of current density 20 mA/cm 2 , 25% HF and etching time 15min. Carbon Nano layers have been deposited on PSi substrate by PECVD. XRD spectrum show that porous silicon has crystalline phase and becomes very broad after etching time, in addition, XRD spectrum for carbon layers show several peaks between (2θ=28.25-28.75) which belong to carbon nanotube and these peaks intensity increases with increasing of carbon thickness. Raman spectrum illustrates that peak position was at 516.32nm for porous silicon prepared at etching time 15 min.

Thin films of CdS have been prepared on glass substrates by the chemical spray pyrolysis method at 250oC from solutions including different concentrations of Na2S (0.2 g and 0.4 g). The films were examined using SEM, an X-ray spectrometer, and a UV-Visible spectrometer. The SEM images and X-ray spectrum show that the films were amorphous, and the grain size varied with Na2S concentration. The optical properties and optical constants also varied clearly with Na2S concentration; in addition, the energy gap changed from 2.3 eV to 2.4 eV for both films.

Titanium - polyaniline nanocomposites were prepared using polymerization of aniline. Structure (XRD), surface (SEM, AFM), chemical (FTIR) and optical (UVSpectrophotometer) properties have been examined. The X-ray diffraction pattern observed the PANI/TiO2 has a crystalline nature and the peak intensity increases with TiO2 composition ratio, the results of SEM &AFM images show the surface of TiO2- polyaniline have regular distribution and homogeneous structure. Furthermore, the grain size increases with increasing of TiO2 compositions and was found to be 32.58nm, 36.21nm and 47.67 nm at x= 0.2, 0.5 and 0.8 respectively. Additionally, optical properties show that the absorption intensity increases with TiO2 compositions compared to pure TiO2. Hybrid of TiO2/PANI has a vast attracted attention especially to improve photocatalytic activity. Simple design and high quality of nanocomposites play a crucial role in improving mechanical and physical properties.

In this work, a CdS–MoS₂–rGO thin-film photocathode was fabricated on FTO substrates via a single-ink drop–spin-coating method followed by annealing under N₂ atmosphere. Among the studied compositions, the film containing 86:9:5 wt% of CdS, MoS₂, and rGO exhibited the best photoelectrochemical performance. Under visible-light illumination from a 50 W halogen lamp (~0.25 sun) in neutral electrolyte, the optimized photocathode achieved a photocurrent density of 0.83 mA cm⁻² at −0.75 V vs Ag/AgCl and retained approximately 96% of its initial current during 60 min of chopped-light operation. Electrochemical impedance spectroscopy revealed a reduced charge-transfer resistance (~120 Ω·cm²), while the Tafel slope decreased to ~85 mV dec⁻¹, indicating enhanced charge transfer and hydrogen evolution kinetics. Hydrogen production was confirmed by gas chromatography, with an evolution rate of ~58.6 μmol h⁻¹. The improved performance is attributed to the synergistic roles of CdS as the light absorber, rGO as an electron-transport network, and MoS₂ as a cocatalyst. These results demonstrate the potential of the CdS–MoS₂–rGO architecture for photoelectrochemical hydrogen evolution under neutral conditions.

The main objectives of this research are to fill a gap in PV inspection systems, which consist of convolutional detectors with a limited amount of global context or transformer classifiers with weak localization. Defects micro-cracks, broken electrodes, and degradation regions decrease the PV energy efficiency considerably, and this encourages the use of an automated computer vision algorithm for the classification of these defects extracted from electroluminescence (EL) images. These objectives were achieved by solving the following task: a novel dual-stage architecture is used to solve these tasks, which are performed sequentially, YOLOv8m followed by the Swin Transformer architecture. In comparison with previous CNN-Transformer PV models that combine features by concatenation or averaging with predefined weights, the proposed model implements a learnable cross-attention fusion probability merging ensemble, which dynamically weights the localization and global attention evidence for each defect. YOLOv8m localizes the exact positions of defects, then the Swin Transformer analyzes the region by window-swiping self-attention to return a long-range degradation pattern that CNNs can't pass. The model was trained using EL images in 12 classes, such as, intact cells, cracks, point defects, finger damage, and EL images were augmented to overcome the class imbalance problem and cross-conditioning generalization. The most important results are a macro-averaged F1-score of 94.05% and 95.40% accuracy, with per-class F1 ranging from 98.98% (Mono-crystalline) to 89.04% (Micro-crack). The significance of the obtained results lies in the use of integrative localization-driven YOLOv8m convolutional features with transformer global attention and learnable cross-attention fusion instead of static cross-attention fusion, providing a new and more robust implementation to perform automated PV quality assessment than a single branch.