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Organic thermoelectric (TE) materials are interesting candidates for wearable energy harvesting systems because they are flexible and convert thermoelectric energy. Thermally conductive flexible thermoelectric generators (TEGs) built of these materials produce power from body and environmental temperature differences, enabling devices to run without recharging. The exceptional electrical conductivity, mechanical flexibility, and environmental robustness of organic transition metal (TE) materials like PEDOT and graphene composites are becoming more known. Due to their synergistic link, PEDOT and graphene layers are appealing thermoelectric possibilities because they increase See beck coefficient and electrical conductivity. Vapor phase polymerization (VPP) may replace chemical or electrochemical polymerization for PEDOT manufacture. This method allows one to accurately regulate PEDOT layer electrical characteristics by altering thickness and structure. These PEDOT and graphene/PEDOT composites were made using iron (III) to sylate as the VPP oxidizing agent. VPP technique produces homogeneous films with better electrical conductivity than existing approaches. We tested the electrical resistance and Linseis LSR-3/800 Seebeck coefficient of PEDOT and graphene/PEDOT composites to determine their thermoelectric characteristics. VPP’s graphene/PEDOT composites have better electrical conductivity and See beck coefficient than conventional PEDOT, improving their thermoelectric power factor. These results show that VPP may be utilized to develop organic thermoelectric materials for lightweight energy collectors.

The textile industry is a very resource-intensive sector, considerably contributing to environmental deterioration via substantial use of water, electricity, and chemicals. With the global transition towards sustainable practices, the notion of a circular economy has become more significant, focusing on prolonging product lifespans, reducing waste, and facilitating material recovery. An effective method for achieving circularity in the textile sector is Design for Disassembly (DfD), which facilitates the straightforward deconstruction of items for recycling or reuse. This study examines the use of Design for Disassembly (DfD) in textile production, emphasizing methods that enable the straightforward separation of fibers, trimmings, and fasteners, hence improving garment recyclability. Data was gathered via a comprehensive examination of textile product designs, production methodologies, and recycling methods throughout the sector. Findings demonstrate that the use of DfD may substantially decrease waste and enhance material recovery rates. Nonetheless, obstacles like redesign expenses, customer awareness, and compatibility with existing production techniques persist. This study presents a detailed technique for incorporating Design for Disassembly (DfD) into textile manufacturing and examines its possible effects on the circular economy. The results substantiate the assertion that the implementation of DfD may be a crucial advancement toward attaining sustainability in the textile sector.

This study examines the potential for enhancing carbon capture and storage (CCS) processes by machine learning to markedly improve performance across diverse capture methods, including as absorption, adsorption, membrane separation, and cryogenic distillation. Through the systematic adjustment of critical operating parameters, including temperature, pressure, flow rates, and sorbent characteristics using machine learning algorithms, we saw significant improvements in CO₂ collection efficiency. The use of optimum operating parameters, namely a temperature range of 40-60°C for absorption and a pressure range of 3-5 bar for adsorption, resulted in a 30% enhancement in capture efficiency. Moreover, machine learning models, namely Random Forest and Support Vector Machines (SVM), achieved a maximum enhancement of 20% in forecasting ideal operating parameters for membrane separation and cryogenic systems. Reduced cycle durations in adsorption processes, facilitated by predictive modeling, resulted in a 15% improvement in CO₂ removal rates. The models’ capacity to forecast sorbent regeneration conditions led to a 10% decrease in energy use. Machine learning algorithms adeptly optimized process-specific parameters, including material composition and flow dynamics, enhancing membrane performance by 18% and cryogenic systems by 12%. These results highlight the significance of using machine learning to customize CCS methods for particular materials and situations, facilitating more sustainable, efficient, and scalable carbon capture systems.

This study presents a novel Fuzzy Logic-Based Energy Management model that aims to optimize the incorporation of renewable energy sources into smart grids. The research used simulated data to evaluate the model's performance in important metrics, revealing significant improvements in the consumption of renewable energy, stability of the grid, dependability of energy storage, and overall efficiency of the system. The Fuzzy Logic Controller adjusts energy distribution in response to current inputs, leading to a significant 20% improvement in the usage of renewable energy. The capacity to adapt is crucial in dealing with the inherent fluctuation of solar, wind, and biomass sources. The approach greatly improves grid stability, as shown by a 15% decrease in grid frequency variations, highlighting its efficacy in assuring a more regulated and steady electricity supply. Furthermore, the dependability of energy storage systems exhibits a notable 25% enhancement in the state of charge, suggesting optimal cycles of charging and discharging. This increased dependability enhances the stability of the energy supply during times of high demand and variations in the power system. The Fuzzy Logic-Based Energy Management model exhibits a significant 22% improvement in total system efficiency when compared to conventional management systems. This indicator encompasses the model's combined effect on the usage of renewable energy, the stability of the power grid, and the optimization of energy storage. Comparative analyses conducted against traditional control strategies, such as proportional-integral-derivative controllers, consistently demonstrate the superiority of the fuzzy logic approach. This approach results in a 10% decrease in grid frequency deviations, a 15% enhancement in energy storage state of charge, and a 12% boost in overall system efficiency. The resilience of the Fuzzy Logic Controller is highlighted by sensitivity analysis, since it demonstrates consistent performance even when parameters vary significantly. The model's practical usefulness and adherence to ethical principles are further confirmed by validation using real-world data from operational smart grid installations. This study provides valuable insights, establishing the Fuzzy Logic-Based Energy Management model as an innovative method for addressing the difficulties associated with integrating renewable energy into smart grids. This model promotes a more sustainable and efficient energy environment for the future.

This research examines the properties of advanced nanomaterials such as graphene, carbon nanotubes (CNTs), and metal-organic frameworks (MOFs) for use in sustainable energy applications. An examination of material composition shows that graphene has a carbon content of 95%, carbon nanotubes have 90% carbon, and MOFs have a carbon content of 70% combined with 20% nitrogen. Structural analysis reveals the shape and pore structure of the nanomaterials. Graphene and carbon nanotubes have a high level of crystallinity and a restricted range of pore sizes, while MOFs have a lower level of crystallinity and a larger range of pore sizes. The electrochemical performance test confirms that the produced nanomaterials are suitable for energy storage applications. Graphene and carbon nanotubes have specific capacities of 300 mAh/g and 350 mAh/g, respectively, while MOFs have a specific capacity of 250 mAh/g. An environmental impact study has shown that there are differences in energy consumption, waste production, and CO2 emissions when it comes to synthesizing nanomaterials. Among these, graphene synthesis has been shown to have the least negative influence on the environment. This study enhances the comprehension of nanomaterial characteristics and their possible uses in sustainable energy technologies, offering valuable knowledge for the development and improvement of energy storage devices based on nanomaterials.

Socioeconomic inequalities are known to negatively impact anthropometric outcomes among children, particularly in developing countries. This study, therefore, assesses the gap in anthropometric outcomes of children 6–59 months along the ethnicity-based social groups in India using the National Family Heath Survey 2015–16 and 2019–21. The paper utilizes logistic regression models, the exogenous switching treatment effect regression (ESTER) model, and the Blinder-Oaxaca Model to disentangle the role of ethnicity (referred to as caste in India) in influencing child anthropometric outcomes while accounting for socio/economic factors. Approximately 35% of children in the sample were stunted and 20% wasted. Result indicates that despite the progress made in reducing child undernutrition between the two survey periods, there is a higher risk of chronic growth faltering (stunting) and underweight in socially disadvantageous groups, and these ethnicity-based disparities exist independent of education and household economic status. To improve children's nutritional status, India needs to develop new nutrition strategies prioritizing double-duty action due to the persistence of undernutrition and rising overweight/obesity among children. The study suggests a need for a distinguished understanding of the underlying causes of chronic and acute forms of malnourishment, and separate interventions are required to reduce the disparities among disadvantaged groups, particularly in tribal communities. [ABSTRACT FROM AUTHOR]

The proposed systems can improve cyber security in VANET applications by enabling efficient detection of complex attacks on the RSU component. The subsequent sections discuss the systems that are applied and support the suggestions for improving the VANET trustworthiness. VANETs and show that the utilization of Cross-Layer Adaptive GNNs can improve cyber security and LEARNING in VANET-based RSUs. As a result, the suggested system can provide robust ways for detecting cyber-attacks by: modeling the network architecture using graphs while combining information regarding several protocol layers to detect complicated interactions between the network entities and find the abnormal activities. the nature of the GNN enables it to update in real-time by adapting to the evolving attack patterns and the shifting network conditions, making them sturdy and flexible defense ways for cyber security. The proposed network e systems can efficiently detect multiple cyber threats and focus on reducing the number of false positives while maintaining low computation costs. Therefore, chances are that incorporating the Cross-layer adaptive GNNs into the RSUs can improve cyber security in VANETs, enhancing the robustness and reliability of prospective smart transportation systems. [ABSTRACT FROM AUTHOR]