ScholarSphere: IITJ Research Insights Hub

Metal halide perovskites (MHPs) are promising photovoltaic materials, yet their practical application is limited by poor long-term stability, crystal and interfacial defects, and abundant non-radiative recombination pathways. Chalcogenide perovskites, particularly BaZrS3, offer a stable and environmentally robust alternative. This study investigates the optoelectronic properties of BaZrS3 using the HSE06 hybrid functional within the density functional theory framework, implemented through VASP, and evaluates its suitability for solar-cell applications. BaZrS3 exhibits a wide bandgap, high absorption coefficient, large refractive index, low reflectivity, and minimal energy loss in the visible region, making it a promising absorber layer. SCAPS-1D simulations identify FTO/SnS2/CdS/BaZrS3/CuSbS2/Au as an optimized device with 13.02% efficiency. Incorporating Ag-doped CZTS as a co-absorber and Cu2BaSnS4 as an interfacial layer raises efficiency to 17.27%. A 1:1 tandem BaZrS3/Ag:CZTS solar cell further enhances efficiency to 25.39%. © 2026 Elsevier B.V.

Accurate and real-time State-of-Charge (SOC) estimation is critical for ensuring the safety, reliability, and longevity of lithium-ion batteries in consumer electronics. While recent SOC estimation techniques, ranging from hybrid and model-based to data-driven approaches, achieve high accuracy, many suffer from significant computational complexity or lack embedded implementation, limiting their applicability in portable, resource-constrained devices. This paper presents a lightweight and scalable Battery Management System (BMS) tailored for consumer electronics, integrating a hybrid SOC estimation algorithm that combines open-circuit voltage (OCV) and coulomb counting (CC). The proposed system is realized through a custom hardware-firmware co-design on an ESP32-WROOM-32 microcontroller platform, developed using MATLAB/Simulink for rapid prototyping and embedded deployment. The compact PCB integrates high-resolution voltage, current, and temperature sensing, optimized for space and power efficiency. Experimental validation across four lithium-ion cell types and multiple charge-discharge rates (0.2C to 2C) demonstrates reliable real-time operation with SOC estimation errors under 1.29% Mean Absolute Error (MAE) and 1.59% Root Mean Square Error (RMSE). Compared to existing methods, the proposed system achieves a practical balance of accuracy, simplicity, and embedded feasibility, making it highly suitable for next-generation battery-powered consumer devices. © 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

The study of stochastic methods has gained significant attention in recent years due to the increasing need to model uncertainty in engineering systems. Structural components, material properties, and environmental conditions often exhibit inherent randomness, making it essential to incorporate uncertainty quantification techniques for reliable predictions. Advanced approaches such as Monte Carlo Simulation (MCS) and Polynomial Chaos Expansion (PCE) have demonstrated effectiveness in this context; however, their mathematical complexity can pose a steep learning curve for researchers and engineers who are new to the field. This paper addresses this challenge by providing a clear and accessible introduction to these methods through a simple yet illustrative example: a spring-mass system. Using this elementary model, key concepts such as random variable modeling, basis function expansion, and solution convergence are explained in an intuitive manner. The simplicity of the spring-mass system enables a focused exploration of the fundamental principles underlying MCS, Intrusive Polynomial Chaos Expansion (IPCE), and Non-Intrusive Polynomial Chaos Expansion (NIPCE), without the added complexity of more sophisticated structural models. The study evaluates the strengths and limitations of each method in terms of computational efficiency, accuracy, and ease of implementation. Through step-by-step explanations and detailed comparisons, this work serves as a foundational reference for those beginning their exploration of stochastic methods. The insights gained from this simplified model can be extended to more complex engineering problems, such as deflection and eigenfrequency analysis of beams with random elastic modulus, and heat conduction in materials with uncertain thermal conductivity. By presenting the mathematical framework in a structured and comprehensible manner, this study aims to serve as a valuable resource for researchers and students seeking to deepen their understanding of stochastic methods in engineering applications. © The Author(s) 2025

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder for which early risk identification is essential for timely clinical assessment, care planning, and long-term disease management. However, computational modeling of AD remains challenging because clinically meaningful diagnosis cannot be inferred from numerical separability alone; it requires alignment with clinical assessment and biomarker validation. From a computational social systems perspective, a major challenge is the heterogeneity and fragmentation of patient-related information, including wearable actigraphy signals, clinical records, and demographic contexts. To address this challenge, we propose AlzDetect, an enhanced TabNet-based deep learning (DL) framework for screening-oriented AD risk modeling across clinically grounded and wearable-derived data domains. AlzDetect is not presented as a subject-level multimodal diagnostic system; rather, it is evaluated as a heterogeneous data risk stratification framework under domain shift, missingness, and cross-domain robustness settings. The wearable-derived (Google Pixel Watch 2) dataset is used only for feasibility and robustness analysis of behavioral signal modeling, not as biomarker-confirmed evidence for AD diagnosis or clinical staging. AlzDetect provides transparent decision support through TabNet’s sparse attention masks, which identify influential predictors at feature and modality levels. The extensive evaluation shows that AlzDetect achieves 96% predictive accuracy and outperforms representative baselines within the evaluated dataset specific settings, demonstrating strong predictive separability and robustness across heterogeneous AD relevant data sources. © 2026 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

Neurodegenerative disorders such as Parkinson's (PD) and Alzheimer's diseases (AD) are linked with the assembly and accumulation of proteins into structured scaffold called amyloids. These diseases pose significant challenges due to their complex and multifaceted nature. While the primary focus has been on endogenous amyloids, recent evidence suggests that bacterial amyloids may contribute to the development and exacerbation of such disorders. The gut-brain axis is emerging as a communication pathway between bacterial and human amyloids. This review delves into the novel role and potential mechanism of bacterial amyloids in modulating human amyloid formation and the progression of AD and PD.

In the MaxSAT with Cardinality Constraint problem (CC-MaxSAT), we are given a CNF-formula Φ, and a positive integer k, and the goal is to find an assignment β with at most k variables set to true (also called a weight k-assignment) such that the number of clauses satisfied by β is maximized. Maximum Coverage can be seen as a special case of CC-MaxSat, where the formula Φ is monotone, i.e., does not contain any negative literals. CC-MaxSat and Maximum Coverage are extremely well-studied problems in the approximation algorithms as well as the parameterized complexity literature. Our first conceptual contribution is that CC-MaxSat and Maximum Coverage are equivalent to each other in the context of FPT-Approximation parameterized by k (here, the approximation is in terms of the number of clauses satisfied/elements covered). In particular, we give a randomized reduction from CC-MaxSat to Maximum Coverage running in time O(1/ϵ)k · (m + n)O(1) that preserves the approximation guarantee up to a factor of (1 − ϵ). Furthermore, this reduction also works in the presence of “fairness” constraints on the satisfied clauses, as well as matroid constraints on the set of variables that are assigned true. Here, the “fairness” constraints are modeled by partitioning the clauses of the formula Φ into r different colors, and the goal is to find an assignment that satisfies at least tj clauses of each color 1 ≤ j ≤ r. Armed with this reduction, we focus on designing FPT-Approximation schemes (FPT-ASes) for Maximum Coverage and its generalizations. Our algorithms are based on a novel combination of a variety of ideas, including a carefully designed probability distribution that exploits sparse coverage functions. These algorithms substantially generalize the results in Jain et al. [SODA 2023] for CC-MaxSat and Maximum Coverage for Kd,d-free set systems (i.e., no d sets share d elements), as well as a recent FPT-AS for Matroid Constrained Maximum Coverage by Sellier [ESA 2023] for frequency-d set systems.

The technique of micro-exfoliation has gained prominence as a highly effective and adaptable method for exploiting two-dimensional (2D) materials, such as graphene Transition metal dichalcogenides (TMDCs), Borophene, Molybdenum disulfide (MoS2), among others. This paper presents an analysis of optical images and the micro exfoliation technique, focusing on the application to MoS2 and graphene. Additionally, the study investigates the exfoliated sheet of graphene, MoS2, and their hybrid on a (111) crystal plane of silicon wafer. The micro-exfoliation technique employed for MoS2 involves a mechanical process that gently disentangles the layers of MoS2 from the larger crystal structure, resulting in the formation of ultrathin two-dimensional nanosheets. This paper comprehensively analyses the exfoliation processes' mechanisms, emphasizing the intricate relationship between van der Waals forces, interlayer bonding, and external forces. The micro-mechanical exfoliation, a fundamental technique, entails the utilization of adhesive scotch tape to remove monolayers from a large MoS2 crystal delicately. The integration of MoS2 into various applications such as electronics, optoelectronics, sensors, and energy storage devices has been driven by its exceptional properties, including its distinctive electronic, optical, and mechanical characteristics. Furthermore, the ability to adjust the bandgap of MoS2 has created novel opportunities for potential applications in the field of semiconductors. This paper provides a succinct summary of recent studies, that have concentrated on the optical characterization of MoS2 monolayers. Optical and Raman spectroscopy was employed to characterize the 2D sheets of MoS2 and its hybrid materials.