Patliputra University

Lead-free chalcogenide perovskites are emerging as promising alternatives to hybrid halide perovskites due to their superior thermal stability, non-toxicity, and strong optical absorption. In this study, the photovoltaic performance of single-junction BaHfSe3-based perovskite solar cells (PSCs) with the TCO/TiO2/BaHfSe3/Cu2O/Au configuration is systematically investigated using SCAPS-1D simulations. Device optimization identifies TiO2 and Cu2O as suitable ETL and HTL materials, respectively. The optimized structure—TCO/TiO2 (50 nm)/BaHfSe3 (500 nm)/Cu2O (100 nm)/Au—achieves a power conversion efficiency (PCE) of 24.47% under standard conditions. Simulation results reveal that device efficiency is influenced by absorber thickness and trap density. A detailed temperature-dependent study highlights that photovoltaic parameter efficiency is governed by the barrier alignment at the TCO/ETL interface. For lower TCO (Transparent Conducting Oxide) work functions (3.97–4.07 eV), PCE decreases monotonically with temperature, attributed to the increase in reverse saturation current resulting from a higher intrinsic carrier concentration. By contrast, higher TCO work functions (4.47–4.8 eV) yield an initial increase in efficiency with temperature, driven by reduced barrier height and favorable Fermi level shifts before efficiency declines at further elevated temperatures. These insights underscore the promise of BaHfSe3 as a lead-free, environmentally robust perovskite absorber for next-generation PSCs, and highlight the critical importance of interface engineering for achieving optimal thermal and operational performance.

Abstract A novel CMTS-absorber-based heterojunction Solar Cell (SC) with device structure FTO/ZnO/Buffer layer (BL) /CMTS/Se has been numerically modeled and simulated using SCAPS-1D software. Our aim is to identify the most promising sulphur-based buffer layer that offers high efficiency and lower toxicity, which is essential for reducing the carbon footprint. The primary objective is to enhance the efficiency of the SC by optimizing key photovoltaic (PV) parameters of the corresponding buffer layer (BL), absorber layer (AL), along with interface defect density. From the simulations and the energy band diagram, we found that CMTS-based SC with ZrS $$_{2}$$ buffer layer revealed an impressive power conversion efficiency (PCE). The effects of the front electrode’s work function and operating temperature on the device were also investigated. The findings indicate that the device exhibits greater stability and achieves optimum performance at a temperature of 300 K, with an optimized work function value of 5.9 eV (Se). Furthermore, the effects of Series resistance and Shunt resistance were considered in this study. To gain insight into the built-in potential, the capacitance–voltage (C–V) characteristics were also analysed. Device performance was properly explained by analyzing the electric field, generation rate, and both radiative and nonradiative recombination rates. The simulations achieved SC performances with a PCE of 26.01 %, a fill factor (FF) of 83.21 %, a short-circuit current ( $$J_{sc}$$ ) of 27.93 mA/cm 2 , and an open-circuit voltage ( $$V_{oc}$$ ) of 1.11 V.

Nanomaterials (NMs) are a unique class of materials with at least one dimension between 1 and 100 nm, exhibiting exceptional properties distinct from their bulk counterparts. Their high surface area, as well as tunable physical, chemical, and magnetic behaviors make them valuable across diverse applications. Phase transitions, such as the emergence of magnetism at the nanoscale, highlight their distinctiveness. This review outlines the historical development of NMs and clarifies key terminology. It covers major synthesis techniques, including top-down and bottom-up approaches, and highlights the unique features that define nanoscale matter. A comprehensive overview of various NMs, such as fullerenes, graphene, carbon nanotubes, MXenes, and core–shell nanoparticles (NPs) is presented. The paper concludes with an overview of current challenges and future directions in the field of NMs.

This study presents a multidisciplinary framework for advancing stress detection by integrating Internet of Things (IoT) capabilities with Galvanic Skin Response (GSR) technology. Leveraging IoT for real-time data acquisition and analysis, we enhance GSR sensor functionality. Contributions from clinical psychology focus on elderly populations, providing insights into age-specific stress indicators and mental health correlations. Physics principles optimise sensor accuracy and data fidelity, while computer science provides the framework for data processing, machine learning models, and IoT infrastructure. The linguistic analysis supports psychologists' recommendations, confirming that GSR readings are best when communication stabilises with subjects. This interdisciplinary approach aims to develop a comprehensive system for effective stress monitoring and management. Our results demonstrate that advanced machine learning models, notably the Random Forest model, achieve high predictive performance in analysing stress levels from GSR data. The confusion matrix and classification report validate its efficiency in accurately distinguishing different stress levels. Feature importance analysis reveals that the GSR Stress Value is the dominant predictor, contributing approximately 90 % to stress classification, with age having a minor influence of around <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\sim 10 \%$</tex>. Gender and state contribute negligibly to stress classification. However, a strong positive relationship between GSR and stress levels is confirmed by a correlation analysis with a coefficient of 0.93. Gender-based differences were minimal, though females exhibited slightly higher stress levels in extreme cases, while younger individuals showed greater fluctuations in stress variability. The findings confirm that GSR data is highly reliable for identifying stress levels and analyzing workplace stressors, offering a datadriven approach to stress analysis and management with significant implications for mental health research and wellbeing strategies.

Love is a universal need, essential for human survival and well-being. It is the driving force behind empathy, connection, and humanity itself. When love is absent, individuals experience loneliness, pain, and despair. The themes of love, acceptance, and the longing for connection resonate profoundly in Khaled Hosseini’s novels A Thousand Splendid Suns and The Kite Runner. These stories intricately weave together human experiences, relationships, and the devastating consequences of neglect and betrayal. The study explores the relevance of these themes in today’s world. In an era marked by uncertainty and change, these novels offer a poignant reminder of the importance of love, relationships, and personal connections. In a world where conflicts and wars continue to rage, Hosseini’s novels offer a powerful reminder of the need for love and compassion to bring about peace and understanding. By examining the ways in which these themes are portrayed in literature, we can gain a deeper understanding of the human condition and the ways in which love and relationships shape our lives.

The Autonomous Vehicle is an urban mobility sector user-generative disruptor. The chapter describes the core technology that allows AVs to function—sensory systems, machine learning, and the artificial intelligence that orchestrates the autonomous process itself. Finally, how can ACT align itself with urban systems, not just at pragmatic levels, such as what the unavoidable safety issues end up being and in terms of legislation, but with ACT itself considered an infrastructure? It will also consider how A.V.s may affect traffic patterns, as well as environmental sustainability and equity. This chapter elaborates on the myriad systems that a city employs to ensure that a fleet of vehicles does what it is designed to do, arguing that many details of this framework are what innovators will need to build if they hope to succeed. And it also lays out the immediate benefits the innovations provide to the city itself.

The rapid digitalization of the banking sector has revolutionized operations, significantly improving customer service, operational efficiency, and overall competitiveness. Through the integration of technologies such as artificial intelligence, mobile banking apps, and automation, banks have streamlined processes and reduced the reliance on manual tasks, leading to improved productivity. However, while digital transformation has its benefits, it has also introduced new challenges, particularly concerning the well-being of employees. The increased reliance on digital tools and automation has created heightened expectations, leading to stressors such as heavier workloads, constant learning demands, and job insecurity as automation replaces certain roles. This study explores how digitalization affects work-related stress and job satisfaction among banking employees by conducting both qualitative and quantitative research. By analyzing existing literature on digital transformation’s impact on the workforce, as well as collecting primary data through interviews and surveys, the study identifies key stress factors including the pressure to adapt to new technologies and the fear of job displacement. The findings suggest that while digitalization offers operational efficiency and reduces repetitive tasks, it also increases psychological pressure on employees. To mitigate these negative effects, the study recommends the adoption of effective stress management strategies, such as comprehensive digital training programs, mental health initiatives, and creating a supportive work environment. Ensuring employee well-being is crucial to maintaining job satisfaction and long-term organizational success.