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Manufacturing of ceramic matrix composites (CMCs) with carbon fiber and carbon matrix includes a time- and laborintensive ceramic infiltration step that is responsible for more than half of the total manufacturing cost. To make CMCs cost-competitive in price-sensitive markets, like concentrated solar power, it is essential to develop a CMC manufacturing process that skips the ceramic infiltration process. In this work, we will show how a high-char-yield preceramic resin can eliminate the infiltration step while maintaining material density and evaluate the technoeconomic impact of our CMC manufacturing process. We monitor both morphology and porosity to determine the quality of CMCs made with different preceramic resins and evaluate the impact of polymer infiltration and pyrolyzing cycles. [ABSTRACT FROM AUTHOR]

In recent years, significant progress has been achieved in the field of perovskite solar cells (PSCs), particularly those comprised of organic–inorganic lead halides, resulting in a remarkable record efficiency of 25.20%. However, the persistent issue of lead toxicity poses a considerable barrier to their widespread commercial adoption. To address this challenge, this study focuses on the optimization of lead-free germanium-based halide PSCs using SCAPS-1D simulation software. In our investigation, CsGeI3 serves as the absorber layer, TiO2 functions as the electron transport layer (ETL), Cu2O is utilized as the hole transport layer (HTL), and various metals are employed as the back metal contact (BMC). The optimization of the BMC leads to the establishment of the FTO/TiO2/CsGeI3/Cu2O/Ni structure. Subsequent optimization steps include fine-tuning the thickness of the absorber, ETL, and HTL layers, as well as optimizing acceptor doping and defect density in the absorber and HTL layers. Sequentially, donor doping and defect density in the ETL are investigated. Interfacial defect densities, along with the impact of temperature, series resistance, and shunt resistance on the photovoltaic performance, are also considered. Upon completion of these optimization procedures, the final device exhibits notable performance characteristics, including an open-circuit voltage (VOC) of 1.165 V, fill factor (FF) of 88.52%, short-circuit current density (JSC) of 24.40 mA/cm2, and a power conversion efficiency (PCE) of 25.16%. Furthermore, the optimized structure without Cu2O HTL demonstrates impressive performance, yielding a PCE of 25.15%. The outcomes presented in this study hold promise for the development of advanced lead-free PSCs, paving the way for high conversion efficiencies in future solar energy technologies. [ABSTRACT FROM AUTHOR]

In order to alleviate the existing global energy and environmental difficulties related to the usage of fossil fuels, there is requirement of improved energy alternatives such as limitless solar energy. The creation of low-cost photovoltaic devices and their design are of significant interest which represent the pinnacle of effective solar energy harvesting. PbS colloidal quantum dot solar cell has a promising ability for ultra-thin solar cell applications. In the present work, the device architecture indium tin oxide/SnO2/PbS–TBAI/MoO3/Au has been used in which MoO3 (molybdenum trioxide) is employed as HTL (hole transport layer) with 3.0 eV bandgap and SnO2 (tin (IV) oxide) as ETL (electron transport layer) with 3.5 eV bandgap and PbS–TBAI (tetrabutylammonium iodide) is used as an absorber layer with 1.2 eV bandgap. Further, 1D solar cell capacitance simulation program tool (SCAPS-1D) is used to obtain energy band diagram, quantum energy and current density curve. The achieved value of power conversion efficiency is 30.16%, Voc (open-circuit voltage) is 0.9025 V, Jsc (short-circuit current density) is 38.746 mA/cm2 and fill factor is 86.14%. For further optimization, the impact of temperature, thickness of active layer and work function on the PV parameters has been studied. [ABSTRACT FROM AUTHOR]

This paper established a novel approach for developing simplified yet accurate models using artificial neural networks (ANNs) in industrial environments. It demonstrates that combining nonlinear regression with neural network modeling enhances predictive accuracy while maintaining the inherent simplicity of ANNs. Industrial sectors are increasingly adopting environmentally friendly practices, driven by the recognition that sustainable initiatives can lead to significant and lasting financial benefits rather than merely a sense of ecological duty. Integrating energy efficiency practices offers potential advantages in waste reduction and resource conservation, which can decrease operating expenses over time. This contributes significantly to pollution mitigation by reducing overall energy consumption cost‐effectively. Numerical simulations based on experimental results validate the proposed method, addressing the complexity and accuracy challenges in business models within the energy sector. [ABSTRACT FROM AUTHOR]