Search Results (1,594)

Ternary chalcogenide-based sulfide materials with distorted morphologies such as BaZrS₃, CaZrS₃, and SrZrS₃, have recently gained much attention in optoelectronics and photovoltaics due to their high structural and thermal stability and compatibility with low-cost, earth-abundant synthesis routes. However, their relatively large bandgaps often limit their suitability for near-infrared (NIR) photodetectors. Here, we conducted a comprehensive investigation of SrHfSe₃, a ternary chalcogenide with an orthorhombic crystal structure and distinctive needle-like morphology, as a promising candidate for NIR photodetection. SrHfSe₃ exhibits a direct bandgap of 1.02 eV, placing it well within the NIR range. Its robust structure, high temperature stability, phase stability and natural abundance make it a compelling material for next-generation, self-powered NIR photodetectors. An in-depth analysis of the SrHfSe₃-based photodetector was performed using SCAPS-1D simulations, focusing on key performance metrics such as J–V behavior, photoresponsivity, and specific detectivity. Device optimization was achieved by thoroughly altering each layer thickness, doping concentrations, and defect densities. Additionally, the influence of interface defects, absorber bandgap, and operating temperature was assessed to enhance the photoresponse. Under optimal conditions, the device achieved a short-circuit current density (Jsc) of 45.88 mA/cm², an open-circuit voltage (Voc) of 0.7152 V, a peak photoresponsivity of 0.85 AW⁻¹, and a detectivity of 2.26 × 10¹⁴ Jones at 1100 nm. A broad spectral response spanning 700–1200 nm confirms its efficacy in the NIR region. These results position SrHfSe₃ as a strong contender for future NIR photodetectors and provide a foundation for experimental validation in advanced optoelectronic applications. Full article

Zinc (Zn)-based batteries have attracted significant interest for applications ranging from electric bikes to grid storage because of its advantageous properties like high abundance, non-toxicity and low-cost. Zn offers a high theoretical capacity of two electrons per atom, resulting in 820 mAh/g, making it a promising anode material for the development of highly energy dense batteries. However, the advancement of Zn-based battery systems is hindered by the limited availability of cathode materials that simultaneously offer high theoretical capacity, long-term cycling stability, and affordability. In this work, we present a new mixed material cathode system, comprising of a mixture of manganese dioxide (MnO₂) and copper oxide (Cu₂O) as active materials, that delivers a high theoretical capacity of ~280 mAh/g (MnO₂ + Cu₂O active material) (based on the combined mass of MnO₂ and Cu₂O) and supports stable cycling for >200 cycles at 1C. We further demonstrate the scalability of this novel cathode system by increasing the electrode size and capacity, highlighting its potential for practical and commercial applications. Full article

This Review examines up-to-date advancements in the integration of biomolecules and solar energy technologies, with a particular focus on biohybrid photovoltaic systems. Biomolecules have recently garnered increasing interest as functional components in a wide range of solar cell architectures, since they offer a huge variety of structural, optical, and electronic properties, useful to fulfill multiple roles within photovoltaic devices. These roles span from acting as light-harvesting sensitizers and charge transport mediators to serving as micro- and nanoscale structural scaffolds, rheological modifiers, and interfacial stabilizers. In this Review, a comprehensive overview of the state of the art about the integration of biomolecules across the various generations of photovoltaics is provided. The functional roles of pigments, DNA, proteins, and polysaccharides are critically reported improvements and limits associated with the use of biological molecules in optoelectronics. The molecular mechanisms underlying the interaction between biomolecules and semiconductors are also discussed as essential for a functional integration of biomolecules in solar cells. Finally, this Review shows the current state of the art, and the most significant results achieved in the use of biomolecules in solar cells, with the main scope of outlining some guidelines for future further developments in the field of biohybrid photovoltaics. Full article

Intermediate-band photovoltaics promise single-junction efficiencies that exceed the Shockley and Queisser limit, yet viable material platforms and device geometries remain under debate. Here, we perform comprehensive two-dimensional device-scale simulations using Silvaco Atlas TCAD to analyze p-i-n In_0.20Ga_0.80N solar cells in which the intermediate band is supplied by In_0.35Ga_0.65N quantum dots located inside the intrinsic layer. Quantum-dot diameters from 1 nm to 10 nm and areal densities up to 116 dots per period are evaluated under AM 1.5G, one-sun illumination at 300 K. The baseline pn junction achieves a simulated power-conversion efficiency of 33.9%. The incorporation of a single 1 nm quantum-dot layer dramatically increases efficiency to 48.1%, driven by a 35% enhancement in short-circuit current density while maintaining open-circuit voltage stability. Further increases in dot density continue to boost current but with diminishing benefit; the highest efficiency recorded, 49.4% at 116 dots, is only 1.4 percentage points above the 40-dot configuration. The improvements originate from two-step sub-band-gap absorption mediated by the quantum dots and from enhanced carrier collection in a widened depletion region. These results define a practical design window centred on approximately 1 nm dots and about 40 dots per period, balancing substantial efficiency gains with manageable structural complexity and providing concrete targets for epitaxial implementation. Full article

The BP (Back-Propagation) neural network model (hereafter referred to as the BP model) often gets stuck in local optima when predicting China’s silicon wafer price, which hurts the accuracy of the forecasts. This study addresses the issue by enhancing the BP model. It integrates the principles of genetic algorithm (GA) with particle swarm optimization (PSO) to develop a new model called the GA-PSO-BP. This study also considers the material price from both the supply and demand sides of the photovoltaic industry. These prices are important factors in China’s silicon wafer price prediction. This research indicates that improving the BP model by integrating GA allows for a broader exploration of potential solution spaces. This approach helps to prevent local minima and identify the optimal solution. The BP model converges more quickly by using PSO for weight initialization. Additionally, the method by which particles share information decreases the probability of being confined to local optima. The upgraded GA-PSO-BP model demonstrates improved generalization capabilities and makes more accurate predictions. The MAE (Mean Absolute Error) value of the GA-PSO-BP model is 31.01% lower than those of the standalone BP model and also falls by 19.36% and 16.28% relative to the GA-BP and PSO-BP models, respectively. The smaller the value, the closer the prediction result of the model is to the actual value. This model has proven effective and superior in China’s silicon wafer price prediction. This capability makes it an essential resource for market analysis and decision-making within the silicon wafer industry. Full article

Urban agriculture (UA) is emerging as a promising strategy for sustainable food production in response to growing environmental pressures. Indoor vertical farming (IVF), combining Controlled Environment Agriculture (CEA) with Building-Integrated Agriculture (BIA), enables efficient resource use and year-round crop cultivation in urban settings. This study assesses the environmental performance of a prospective IVF system located on a university campus in Portugal, focusing on the integration of photovoltaic (PV) energy as an alternative to the conventional electricity grid (GM). A Life Cycle Assessment (LCA) was conducted using the Environmental Footprint (EF) method and the LANCA model to account for land use and soil-related impacts. The PV-powered system demonstrated lower overall environmental impacts, with notable reductions across most impact categories, but important trade-offs with decreased soil quality. The LANCA results highlighted cultivation and packaging as key contributors to land occupation and transformation, while also revealing trade-offs associated with upstream material demands. By combining EF and LANCA, the study shows that IVF systems that are not soil-based can still impact soil quality indirectly. These findings contribute to a broader understanding of sustainability in urban farming and underscore the importance of multi-dimensional assessment approaches when evaluating emerging agricultural technologies. Full article

Laser annealing of oxide functional thin films makes them compatible with substrates of various types, especially flexible materials. The effects of optical annealing on Ni-doped ZnO thin films were the subject of investigation and analysis in this study. Using pulsed laser deposition, we deposited polycrystalline ZnNiO films on sapphire and silicon substrates. The deposited film was annealed by laser heating. A continuous CO₂ laser was used for this purpose. The uniformly distributed long-wavelength radiation of the CO₂ laser can penetrate deeper from the surface of the thin film compared to short-wavelength lasers such as UV and IR lasers. After growth, optical post-annealing processes were applied to improve the conductive properties of the films. The crystallinity and surface morphology of the grown films and annealed films were analyzed using SEM, and their electrical parameters were evaluated using van der Pauw effect measurements. We used electrical conductivity measurements and investigated the photovoltaic properties of the ZnNiO film. After CO₂ laser annealing, changes in both the crystalline structure and surface appearance of ZnO were evident. Subsequent to laser annealing, the crystallinity of ZnO showed both change and degradation. High-power CO₂ laser annealing changed the structure to a mixed grain size. Surface nanostructuring occurred. This was confirmed by SEM morphological studies. After irradiation, the electrical conductivity of the films increased from 0.06 Sm/cm to 0.31 Sm/cm. The lifetime of non-equilibrium charge carriers decreased from 2.0·10⁻⁹ s to 1.2·10⁻⁹ s. Full article

Dark asphalt surfaces, absorbing about 95% of solar radiation and warming to 60–70 °C during summer, intensify urban heat while providing substantial prospects for energy extraction. This review evaluates four primary technologies—asphalt solar collectors (ASCs, including phase change material (PCM) integration), photovoltaic (PV) systems, vibration-based harvesting, thermoelectric generators (TEGs)—focusing on their principles, efficiencies, and urban applications. ASCs achieve up to 30% efficiency with a 150–300 W/m² output, reducing pavement temperatures by 0.5–3.2 °C, while PV pavements yield 42–49% efficiency, generating 245 kWh/m² and lowering temperatures by an average of 6.4 °C. Piezoelectric transducers produce 50.41 mW under traffic loads, and TEGs deliver 0.3–5.0 W with a 23 °C gradient. Applications include powering sensors, streetlights, and de-icing systems, with ASCs extending pavement life by 3 years. Hybrid systems, like PV/T, achieve 37.31% efficiency, enhancing UHI mitigation and emissions reduction. Economically, ASCs offer a 5-year payback period with a USD 3000 net present value, though PV and piezoelectric systems face cost and durability challenges. Environmental benefits include 30–40% heat retention for winter use and 17% increased PV self-use with EV integration. Despite significant potential, high costs and scalability issues hinder adoption. Future research should optimize designs, develop adaptive materials, and validate systems under real-world conditions to advance sustainable urban infrastructure. Full article

This research integrates stochastic optimization techniques with robust modeling and probabilistic modeling approaches to enhance photovoltaic cell manufacturing processes and product reliability. The study employed an adapted genetic algorithm to tackle uncertainties in the manufacturing process, resulting in improved operational efficiency. It consistently achieved optimal fitness, with values remaining at 1.0 over 100 generations. The model displayed a dynamic convergence rate, demonstrating its ability to adjust performance in response to process fluctuations. The system preserved resource efficiency by utilizing approximately 2600 units per generation, while minimizing machine downtime to 0.03%. Product reliability reached an average level of 0.98, with a maximum value of 1.02, indicating enhanced consistency. The manufacturing process achieved better optimization through a significant reduction in defect rates, which fell to 0.04. The objective function value fluctuated between 0.86 and 0.96, illustrating how the model effectively managed conflicting variables. Sensitivity analysis revealed that changes in sigma material and lambda failure had a minimal effect on average reliability, which stayed above 0.99, while average defect rates remained below 0.05. This research exemplifies how stochastic, robust, and probabilistic optimization methods can collaborate to enhance manufacturing system quality assurance and product reliability under uncertain conditions. Full article

Transparent conductive oxides (TCOs) have become essential components in a broad range of modern devices, including smartphones, flat-panel displays, and photovoltaic cells. Currently, indium tin oxide (ITO) is used in approximately 90% of these devices. However, ITO prices continue to rise due to the limited supply of indium (In), making the development of alternative materials for TCOs indispensable. Therefore, this study highlights the latest advances in creating new, affordable materials, with a focus on aluminum-doped zinc oxide (AZO). Over the last few decades, this material has been widely studied to improve its physical properties, particularly its low electrical resistivity, which can affect the performance of various devices. Now, it is close to replacing ITO due to several advantages including cost-effectiveness, stability under hydrogen plasma, low processing temperatures, and lack of toxicity. Besides that, in comparison to other TCOs such as IZO, IGZO, or IZrO, AZO achieved a low electrical resistivity (10⁻⁵ ohm cm) while maintaining a high transparency across the visible spectrum (over 85%). Additionally, due to the increasing development of technologies utilizing such materials, it is essential to develop more effective techniques for producing TCOs on a larger scale. Additionally, due to the increasing development of technologies utilizing such materials, it is essential to develop more effective techniques for producing TCOs on a larger scale. This review emphasizes the potential of AZO as a cost-effective and scalable alternative to ITO, highlighting key advancements in deposition techniques such as pulsed laser deposition (PLD). Full article

In this study, lanthanum–nitrogen co-doped titanium dioxide (La-N-TiO₂) thin films were fabricated using Ion Beam Assisted Deposition (IBAD) and subjected to accelerated ultraviolet (UV) aging experiments to systematically investigate the impact of co-doping on the films’ resistance to UV aging. X-ray diffraction (XRD) analysis revealed that La-N co-doping inhibits the phase transition from anatase to rutile, significantly enhancing the phase stability of the films. Scanning electron microscopy (SEM) and atomic force microscopy (AFM) characterizations indicated that co-doping increased the density and surface uniformity of the films, thereby delaying the expansion of cracks and increase in roughness induced by UV exposure. Energy-dispersive X-ray spectroscopy (EDS) results confirmed the successful incorporation of La and N into the TiO₂ lattice, enhancing the chemical stability of the films. Contact angle tests demonstrated that La-N co-doping markedly improved the hydrophobicity of the films, inhibiting the rapid decay of hydrophilicity during UV aging. After three years of UV aging, the co-doped films maintained high structural integrity and photocatalytic performance, exhibiting excellent resistance to UV aging. These findings offer new insights into the long-term stability of photovoltaic self-cleaning materials. Full article

Interest in germanium electronic devices is experiencing a comeback thanks to their suitability for a wide range of new applications, like CMOS transistors, quantum technology or infrared photonics. Among these applications, Ge-based thermophotovoltaic converters could become the backbone of thermo-electrical batteries. However, these devices are still far from the efficiency threshold needed for industrial deployment, with surface recombination as the main limiting factor for the material. In this work, we discuss the main passivation techniques developed for germanium photovoltaic and thermophotovoltaic devices, summarizing their main advantages and disadvantages. The analysis reveals that surface recombination velocities as low as 2.7 cm/s and 1.3 cm/s have already been reported for p-type and n-type germanium, respectively, although improving surface recombination velocities below 100 cm/s would result in marginal efficiency gains. Therefore, the main challenge for the material is not reducing this parameter further but developing robust and reliable processes for integrating the current techniques into functional devices. Full article

We have conducted a first-time investigation into the multiferroic properties and band gap behavior of ${\mathrm{CuFeO}}_2$ doped with Al, Ga, and In ions at the Fe site, employing a microscopic model and Green’s function formalism. The tunability of the band gap across a broad energy spectrum highlights the potential of perovskite materials for advanced applications, including photovoltaics, photodetectors, lasers, light-emitting diodes, and high-energy particle sensors. The disparity in ionic radii between the dopant and host ions introduces local lattice distortions, leading to modifications in the exchange interaction parameters. As a result, the influence of ion doping on various properties of ${\mathrm{CuFeO}}_2$ has been elucidated at microscopic level. Our findings indicate that Al doping enhances magnetization and reduces the band gap energy. In contrast, doping with Ga or In results in a decrease in magnetization and an increase in band gap energy. Additionally, it is demonstrated that ferroelectric polarization can be induced either via external magnetic fields or by Al substitution at the Fe site. The theoretical results show good qualitative agreement with experimental data, confirming the validity of the proposed model and method. Full article

Improving photovoltaic (PV) panel performance under extreme climatic conditions is critical for advancing sustainable energy systems. In hyper-arid regions, elevated operating temperatures significantly reduce panel efficiency. This study investigates and compares three cooling techniques—air cooling, water cooling, and porous media cooling—using thermal and electrical modeling based on CFD simulations in ANSYS. The numerical model replicates a PV system operating under peak solar irradiance (900 W/m²) and realistic ambient conditions in Adrar, Algeria. Simulation results show that air cooling leads to a modest temperature reduction of 6 °C and a marginal efficiency gain of 0.25%. Water cooling, employing a top-down laminar flow, reduces cell temperature by over 35 °C and improves net electrical output by 30.9%, despite pump energy consumption. Porous media cooling, leveraging passive evaporation through gravel, decreases panel temperature by around 30 °C and achieves a net output gain of 26.3%. Mesh sensitivity and validation against experimental data support the accuracy of the model. These findings highlight the significant potential of water and porous material cooling strategies to enhance PV performance in hyper-arid environments. The study also demonstrates that porous media can deliver high thermal effectiveness with minimal energy input, making it a suitable low-cost option for off-grid applications. Future work will integrate long-term climate data, real diffuser geometries, and experimental validation to further refine these models. Full article

Accurate identification of active-layer compositions in organic photovoltaic (OPV) devices often relies on invasive techniques such as electrical measurements or material extraction, which risk damaging the device. In this study, we propose a non-invasive classification approach based on simulated full-device absorption spectra. To account for fabrication-related variability, the active-layer thickness varied by over ±15% around the optimal value, creating a realistic and diverse training dataset. A multilayer perceptron (MLP) neural network was applied with various activation functions, optimization algorithms, and data split ratios. The optimized model achieved classification accuracies exceeding 99% on both training and testing sets, with minimal sensitivity to random initialization or data partitioning. These results demonstrate the potential of applying deep learning to spectral data for reliable, non-destructive OPV composition classification, paving the way for integration into automated manufacturing diagnostics and quality control workflows. Full article