Search Results (185)

Hard and brittle material components with complex curved surfaces are widely used in critical foundational parts within aerospace, optoelectronics, and other fields. Their machining quality directly determines the performance and reliability of high-end equipment. However, the inherent properties of hard and brittle materials make them prone to surface/subsurface damage during traditional polishing processes, and maintaining the form accuracy of complex curved surfaces is challenging. Although abrasive waterjet polishing enables non-contact flexible processing, its energy efficiency is low. Additionally, although ultrasonic-assisted polishing can improve material removal, its spatial localization is insufficient, limiting energy utilization efficiency. To address these issues, this paper proposes a novel method of focused, ultrasonic, vibration-assisted abrasive waterjet polishing. The influence of the radiation force and cavitation force of the focused ultrasonic field on abrasive particle motion is analyzed, and analytical equations for abrasive particle velocity are established. Subsequently, single-factor and response surface methodologies are employed to systematically evaluate the influence of process parameters on machining quality and efficiency. The material removal process during FUAP involves both plastic shearing/chip formation and localized brittle fracture. Focused ultrasonic assistance promotes micro-cutting and plastic shearing, while localized crushing pits indicate that brittle fracture remains non-negligible. The focused ultrasound superimposes alternating stress onto the impact action, mitigating microscale crushing pit defects during the brittle removal process of monocrystalline silicon. Furthermore, appropriately increasing ultrasonic power, enlarging abrasive particle size, and raising abrasive concentration all contribute to enhanced material removal from monocrystalline silicon. Adjusting the nozzle height to the effective region of the focused ultrasonic energy field promotes material removal via chip formation while avoiding pit defects caused by excessive fracture. These results suggest that focused ultrasonic energy can be effectively integrated into abrasive waterjet polishing to enhance material removal while suppressing brittle surface defects, thereby offering a promising strategy for the ultra-precision finishing of hard and brittle components with complex curved surfaces. Full article

This study investigates the material removal mechanism and the evolution of subsurface damage (SSD) in single-crystal silicon during ultra-precision machining using molecular dynamics (MD) simulations. A three-dimensional MD model was established by employing Tersoff and Morse interaction potentials to evaluate the effects of different cutting strategies on cutting response, stress distribution, surface morphology, and defect evolution. The results show that the multi-pass cutting strategy effectively reduces the mean cutting force and suppresses severe stress concentration regions exceeding 7 GPa. This improvement is mainly attributed to the progressive release of residual stress and the more gradual removal of material during successive cutting passes. The formation of SSD is dominated by lattice distortion and amorphous phase transformation, both of which are closely associated with localized high von Mises stress beneath the machined surface. Further analyses of surface morphology and defect density indicate that a multi-pass strategy with a single-pass cutting depth below 1 nm provides a favorable balance between machining efficiency and surface integrity. These findings provide atomistic insights into damage suppression and process optimization for the ultra-precision machining of brittle semiconductor materials. Full article

The developed spectrally selective open-circuit voltage decay (OCVD) system uses pulsed LEDs to probe carrier recombination at varying depths in monocrystalline and multicrystalline silicon solar cells. Full-size c-Si cells exhibit a 15 ms voltage decay under broad-spectrum illumination, while 2 × 2 cm² mini-cells with a 5 mm LED spot reveal localized decays around 300 μs. Low-temperature annealing at 115 °C for 3 min enhances surface passivation, shown by increased decay times under blue (458 nm) excitation. In contrast, infrared (864 nm) excitation indicates minimal improvement in bulk carrier lifetime. This technique effectively differentiates surface from bulk recombination, providing valuable insights for optimizing passivation strategies in silicon solar cells. Full article

Sustainable and circular materials for renewable energy applications are essential for reducing the life-cycle burdens of photovoltaic (PV) power plants and improving the resource efficiency of low-carbon energy infrastructure. This study assesses the material-related environmental performance of an existing 2 MW mono-crystalline silicon (sc-Si) photovoltaic power plant in northern Poland and a prospective perovskite solar cell (PSC) module scenario modelled as an equivalent system with the same location, installed capacity, and annual electricity output. The functional unit was defined as 2000 MWh of electricity delivered annually. A cradle-to-grave life cycle assessment (LCA) was performed in SimaPro 9.4.0 using the ReCiPe 2016 method, complemented by an Intergovernmental Panel on Climate Change (IPCC)-based greenhouse gas assessment. The inventory included photovoltaic modules, support structures, electrical installations, inverter stations, and transformers, with landfill and recycling-oriented material recovery considered as alternative post-consumer management strategies for materials after the end of the technical facility’s life. The results show that material-intensive upstream production stages and key balance-of-system components are major contributors to life-cycle impacts, while recycling can reduce selected burdens through material recovery and avoided production of primary materials. These recycling benefits were modelled using material-specific recovery rates and avoided-production credits assigned only to recovered fractions assumed to meet secondary material quality requirements. Under the adopted modelling assumptions, the PSC module scenario indicates potential for lower life-cycle impacts than the sc-Si baseline. For the prospective perovskite module scenario, this potential benefit is conditional on intact encapsulation during operation and controlled collection, separation, and recovery of lead-containing fractions at the end of life. The study demonstrates that material composition, component design, and circular end-of-life management are decisive factors for improving the environmental performance of PV power plants. Full article

Corrosion of carbon steel components represents a major economic and safety challenge in industrial applications, motivating the development of protective thin film coatings with optimized deposition parameters. This study investigates the deposition of nickel nitride (Ni-N) thin films on AISI 1016 carbon steel and silicon (111) wafers by reactive radio-frequency (r.f.) magnetron sputtering at three power levels: 150, 175, and 200 W. Surface color, film thickness, roughness, crystal structure, mechanical properties, and electrochemical behavior were evaluated using optical microscopy, stylus profilometry, atomic force microscopy (AFM), X-ray diffraction (XRD), nanoindentation, and potentiodynamic polarization combined with electrochemical impedance spectroscopy (EIS). Increasing r.f.-power produced systematic surface color changes consistent with variations in film thickness, which ranged from approximately 25.0 to 50.7 nm. Higher deposition power promoted smoother surfaces, with average roughness (Ra) decreasing from 64.28 nm at 150 W to 20.62 nm at 200 W. XRD analysis revealed a monocrystalline Ni₃N hexagonal close-packed (HCP) phase at 150 W, transitioning to a dual-phase Ni₃N (HCP) and Ni₄N face-centered cubic (FCC) microstructure at 175 and 200 W. The highest hardness (11.80 ± 3.34 GPa) was recorded at 150 W, accompanied by pop-in events attributed to dislocation nucleation in the HCP lattice. Electrochemical evaluation in 3.5 wt.% NaCl solution demonstrated that films deposited at 150 and 175 W exhibited corrosion current densities and rates exceeding those of bare steel, confirming that these conditions accelerate rather than inhibit corrosion. Only the film deposited at 200 W achieved superior corrosion protection, with a corrosion current density and rate approximately 50% lower than bare steel, attributed to its denser microstructure and smoother surface morphology. These findings demonstrate that r.f. power is a critical parameter governing the properties of Ni-N thin films, and that careful optimization of deposition conditions is essential before recommending such coatings for industrial corrosion-protective applications. Full article

Monocrystalline silicon, with its high refractive index, high infrared transmittance, and excellent dimensional stability, serves as a key optical component in high-energy laser systems, infrared imaging, and guidance fields. Its processing quality directly affects the performance indicators of related systems. To address the challenges of nanoscale precision shape and property control during processing, methods such as ultra-precision cutting, magnetorheological polishing, laser micromachining, ion beam processing, plasma etching, and chemical–mechanical polishing have been adopted to improve the surface shape accuracy and repair defects of monocrystalline silicon components. This paper reviews the research progress of key technologies, including nanoscale precision surface shape control manufacturing technology, nanoscale precision property control generation methods, and combined processes for its nanoscale shape and property control, providing technical support for achieving nanoscale precision shape and property control manufacturing of monocrystalline silicon components. Full article

Shape-from-Focus (SFF) is attractive for microscopic three-dimensional measurement, but high dynamic range (HDR) surfaces and weak-textured surfaces distort the focus curve through saturation, spurious peaks, and low signal-to-noise ratios. These distortions violate the unimodal assumption used by Gaussian peak localization and limit post-processing-only correction. This paper proposes a physics-guided distortion-aware SFF pipeline for opaque single-surface targets. The Distortion-Aware Focal Depth Regression Network (DAFDR-Net) learns from synthetic focus-curve distortions and uses Channel-wise Feature Attention (CFA) and Soft Peak Localization to reweight distortion-sensitive temporal-response features while preserving a peak-localization prior. Its foreground validity output is further used for confidence-guided adaptive smoothing. On an HDR free-form surface dataset, the proposed pipeline reduces RMSE by 36.5% relative to an MRF optimization method and compresses the 99th-percentile absolute error from 0.181 to 0.033. On weak-textured monocrystalline silicon wafer data, it reduces flat-region depth standard deviation by 51.3%. Full article

Ultra-precision thinning of hard and brittle materials like monocrystalline silicon demands high dynamic stability in thinning spindle. To address the challenge of accurately detecting subtle spindle chatter anomalies in industrial environments characterized by high noise and limited data, this paper proposes a physics-guided dual-stream attention fusion transfer network (PG-AFNet). First, a physics-guided signal preprocessing method was developed. Using variational mode decomposition (VMD) and continuous wavelet transform (CWT) masking, one-dimensional dynamic features and high-frequency regions of interest (ROIs) rich in transient impact features were extracted. Second, the PG-AFNet architecture was designed. By introducing an attention mechanism, it achieves deep integration of one-dimensional purely dynamic sequences with two-dimensional spatiotemporal visual textures to capture surface damage features caused by subtle vibrations. Finally, systematic validations were conducted using a real silicon wafer thinning dataset with 197 real samples. By overcoming small-sample limitations via physical augmentation, PG-AFNet achieved an 82.45% (86.64% after data augmentation) diagnostic accuracy, significantly outperforming traditional baselines. Furthermore, a large-scale cross-load validation on the diverse CWRU dataset yielded an exceptional 99.68% accuracy under mixed-load conditions, conclusively verifying the model’s robust domain generalization. Lastly, a rigorous ablation study explicitly quantified the indispensable contributions of the physics-guided dual-stream architecture and attention fusion. This research provides a feasible theoretical foundation for intelligent surface quality monitoring in semiconductor hard-brittle material processing. Full article

This study investigates the long-term performance degradation and forecasting of three silicon-based photovoltaic technologies—polycrystalline (pc-Si), monocrystalline (mc-Si), and amorphous silicon (a-Si)—using a seven-year dataset (2015–2021) from a semi-arid climate. Degradation rates are quantified through seasonal-trend decomposition and Arrhenius analysis, revealing distinct mechanisms: pc-Si exhibits the lowest annual degradation (0.36%/year), followed by a-Si (0.57%/year), while mc-Si shows the highest (0.77%/year), with a notable thermal annealing effect partially compensating degradation in a-Si. For forecasting performance ratio, four models are compared, where long short-term memory networks achieve the highest accuracy by capturing nonlinear temporal dependencies, while SARIMA offers robust, interpretable results with lower complexity. Beyond predictive performance, the study establishes links between model behavior and underlying physical processes such as degradation and annealing, and analyzes prediction uncertainty in relation to temperature variability and dust accumulation. These findings highlight trade-offs between accuracy, interpretability, and deployment feasibility, providing a framework for PV performance forecasting under univariate, semi-arid conditions, with future work directed toward multivariate, physics-informed approaches across broader technologies and climates. Full article

Nitridation of different materials using ion implantation is of considerable interest for many applications. As electronic components, oxynitride (SiO_xN_y) layers exhibit beneficial properties such as precise compositional variability, refractive index tunability, oxidation resistance, and low mechanical stress. In the present study we investigate nanoscale SiO_xN_y synthesized using ion implantation methods. To introduce N⁺ ions into a shallow Si subsurface region, both conventional ion beam implantation and plasma immersion ion implantation with subsequent high-temperature treatment in dry O₂ are used. The optical and morphological properties and chemical bonding of formed SiO_xN_y layers were studied by applying spectroscopic ellipsometry in the range of VIS-Near IR (SE) and IR (IR-SE), Raman spectroscopy and Atomic Force Microscopy (AFM). Monte Carlo modeling of implant profiles contributed to understanding physical and chemical processes and predicted different influences of the incorporated N⁺ ions on the oxidation mechanism, confirmed by the thickness dependence of SiO_xN_y/Si layers obtained from the SE data analysis. IR-SE spectral analysis established the formation of Si-O, Si-N, Si-N-O and Si-Si chemical bonds in the grown layers. The occurrence of amorphization of the Si crystal lattice due to incorporation of high-energy N⁺ ions into the Si lattice is confirmed by the Raman and ellipsometry results. The free Si atoms can congregate, forming nanocrystalline clusters. AFM imaging revealed that both implantation methods left the surface of the resulting SiO_xN_y layers considerably smooth with similar roughness parameter values. The results of the studies imply that the technological approaches used allow the production of high-quality nanoscale silicon oxynitride films with appropriate tunable composition and properties for possible application in advanced electronic devices for nanoelectronics, optoelectronics and sensor applications. Full article

Chiral bound states in the continuum (BIC) metasurfaces have emerged as a promising platform for enhancing light–matter interactions, which have potential applications in advanced photonic and quantum information devices. However, simultaneously achieving near-perfect circular dichroism and highly efficient nonlinear conversion with highly symmetric structures in metasurfaces remains an open challenge. In this work, we design a $C_4$-symmetric chiral metasurface composed of eight elliptical silicon nanorods on a ${\mathrm{SiO}}_2$ substrate, where monocrystalline silicon is used as the nonlinear optical material. By combining simulations and nonlinear time-domain coupled-mode theory (TCMT), we discovered that both the optimal chirality and the nonlinear conversion efficiency can be attained simultaneously due to the critical coupling between the metasurface mode and the quasi-BIC mode. Meanwhile, a near-perfect circular dichroism (CD = 0.99) and a high nonlinear conversion efficiency of $7\times {10}^{-5}$ under a radiation intensity of $5{\mathrm{kW}/\mathrm{cm}}^2$ are numerically achieved due to the robustness of bound states in the continuum. This work offers a promising route toward high-performance chiral nonlinear photonic components, which is of great importance for the development of ultra-compact optical devices such as circular polarization detectors, chiral sensors, and nonlinear photonic chips for integrated optical and quantum information systems. Our research not only contributes to the fundamental understanding of chiral metasurfaces but also provides a practical approach for achieving high-efficiency nonlinear optical devices. Full article

Accurate identification of photovoltaic (PV) cell and module parameters remains a fundamental yet challenging task, particularly as model complexity increases from five to nine unknown parameters. In this study, the parameter extraction problem is rigorously formulated as a nonlinear optimization task and addressed using a novel hybrid metaheuristic algorithm, termed the Shuffled Frog Leaping–Shuffled Complex Evolution (SFL-SCE) method. The proposed approach synergistically integrates the population-based social learning mechanism of the Shuffled Frog Leaping Algorithm (SFL) with the robust global search and refinement capabilities of Shuffled Complex Evolution (SCE), thereby achieving an effective balance between exploration and exploitation. The SFL-SCE algorithm minimizes the root-mean-square error (RMSE) between measured and simulated current–voltage characteristics and is systematically applied to three widely used PV technologies: the RTC-France silicon solar cell, the polycrystalline Photowatt-PWP201 module, and the monocrystalline STM6-40/36 module. For each device, parameter identification is performed under one-diode, two-diode, and three-diode modelling frameworks, encompassing increasing levels of physical fidelity and computational complexity. Experimental data are employed throughout to ensure practical relevance and robustness. The performance of the proposed algorithm is comprehensively evaluated against its constituent algorithms (SFLA and SCE) as well as several state-of-the-art hybrid optimization techniques reported in the literature. Comparative results demonstrate that SFL-SCE consistently achieves superior accuracy, enhanced reliability, and faster convergence, as evidenced by lower minimum, mean, and maximum RMSE values, reduced standard deviation, and improved convergence behavior across all test cases. These findings confirm the effectiveness of the proposed hybridization strategy and establish SFL-SCE as a powerful and reliable tool for high-precision PV model parameter identification. Full article

Diamond wire sawing, as the core process for monocrystalline silicon wafering, has gained widespread application in the photovoltaic and microelectronics industries due to its high efficiency and low material loss. This study investigates the cutting mechanism of monocrystalline silicon with (100) crystal orientation under multi-abrasive and multi-scratch conditions using explicit finite element dynamics simulation. It focuses on analyzing the effects of radial spacing and height difference between abrasive grains on surface morphology, cutting force, and residual stress. Based on the Johnson-Holmquist-II (JH-II) constitutive model, a high-precision three-dimensional finite element simulation model was constructed. Simulation results indicate that the spacing and height difference between abrasive grains significantly affect the grain-to-grain coupling, thereby influencing the peak cutting force and the surface damage characteristics of the scratches. To address cutting force and residual stress responses, this study proposes an algorithmic optimization scheme based on a multifactor orthogonal experimental design. The analysis indicates that the optimal parameters—U = 1385 m/min, V = 142°, and W = 6.2 μm—reduce residual stress by 33% and cutting force by 75%. Full article

This study examines the functional degradation of crystalline silicon photovoltaic cells after 17 years of field exposure in the Adrar Desert, Algeria. Harsh thermal, radiative, and mechanical conditions accelerate aging, affecting electrical performance and structural stability. Monocrystalline silicon cells were extracted and analyzed by scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), Raman spectroscopy, electrical resistivity measurements, and vibrating sample magnetometry (VSM). SEM revealed microcracks, delamination, and corrosion products. EDS showed Ag, Si, O, and C signals, while Raman indicated silicon features and signatures consistent with encapsulant (EVA) degradation. The temperature-dependent resistivity displayed a dual behavior with a minimum near ~72 °C, above which resistivity increased, consistent with a transition in the dominant transport mechanisms. VSM measurements showed an overall diamagnetic response with a weak hysteresis loop suggestive of defect-related contributions. The observed aging is primarily associated with oxidation, metal migration, and encapsulant degradation. These findings motivate more robust materials and interfaces for desert climates, alongside improved thermal management and active monitoring. Full article

This study investigates the influence of array height, irradiance, and wind speed on temperature difference and thermal gradients in photovoltaic (PV) arrays operating in hot, arid conditions. A field experiment was conducted in Mesa, Arizona (latitude 33° N), using two fixed-tilt PV module arrays installed at different elevations—one at 1 m and the other at 2 m above ground level. Each array comprised seven monocrystalline PV modules arranged in a single row with an 18° tilt angle optimized for summer performance. Data were collected between June and September 2025, and the analysis was restricted to 10:00–13:00 h to avoid shading and ensure uniform irradiance exposure on both arrays. Measurements included module backsheet temperatures at the center and edge modules, ambient temperature, plane-of-array (POA) irradiance, and wind speed. By maintaining identical orientation, tilt, and exposure conditions across all PV configurations, the influence of array height was isolated by comparing module operating temperatures between the 1-m and 2-m installations (inter-array comparison). Under the same controlled conditions, the setup also enabled an examination of how the intra-array comparison affects temperature gradients along the PV modules themselves, thereby revealing edge-center thermal non-uniformities. Results indicate that the 2 m array consistently operated 1–3 °C cooler than the 1 m array, confirming the positive impact of elevation on convective cooling. This reduction corresponds to a 0.4–0.9% improvement in module efficiency or power based on standard temperature coefficients of crystalline silicon modules. The 1 m array exhibited a mean edge–center intra-array temperature gradient of −1.54 °C, while the 2 m array showed −2.47 °C, indicating stronger edge cooling in the elevated configuration. The 1 m array displayed a broader temperature range (−7 °C to +3 °C) compared to the 2 m array (−5 °C to +2 °C), reflecting greater variability and weaker convective uniformity near ground level. The intra-array temperature gradient became more negative as irradiance increased, signifying intensified edge cooling under higher solar loading. Conversely, wind speed inversely affected ΔT, mitigating thermal gradients at higher airflow velocities. These findings highlight the importance of array height (inter-array), string length (intra-array), irradiance, and wind conditions in optimizing PV system thermal and electrical performance. Full article