ScholarSphere: IITJ Research Insights Hub

The pursuit of alternative anode materials capable of providing higher energy density in lithium-ion batteries (LIBs) represents a crucial challenge for both industrial and academic sectors. Tin-based anodes have drawn considerable interest because of their commercial abundance and high theoretical capacities. However, their widespread acceptance is hindered by prominent volume expansion and consequent structural degradation during cycling in liquid-electrolyte LIBs. Organic–inorganic hybrids are an important class of functional materials, as they combine the characteristics of inorganic and organic parts and lead to new properties through their cooperative interactions. In this context, this study presents the incorporation of hexachlorostannate based on formazan, [LH]2[SnCl6] (1), where L is 1,5-diphenyl-3-(4-pyridyl)formazan, as a potential anode material for LIBs, which have been developed by the room temperature reaction of SnCl4 with formazan ligand in the presence of HCl. The molecular structure of complex 1 was obtained using single-crystal XRD and further characterized using various techniques. The electrochemical results of the synthesized organic–inorganic hybrid showed a reversible redox behavior for Li+ ion battery, revealing a specific capacity of 145.5 mAh g−1 after 100 cycles at 50 mA g−1. This work provides a new foundation for exploring the electrochemical potential of formazan-based organic–inorganic hybrid ionic systems. © 2026 Wiley-VCH GmbH.

A series of 4-aryltetrazolo[1,5-a][1,8]naphthyridine derivatives was synthesized and confirmed by 1H, 13C NMR, and mass spectrometry. The synthesized derivatives’ antibacterial activity was assessed against gram-positive bacteria (Staphylococcus aureus and Bacillus cereus) and gram-negative bacteria (E. coli and Klebsiella pneumoniae). All the compounds exhibited excellent activity. Among the synthesized derivatives, 4-(p-tolyl)tetrazolo[1,5-a][1,8]naphthyridine showed the most potent antibacterial activity. Molecular docking studies were a good complement, and the binding interactions of the derivatives with relevant bacterial target proteins revealed a strong correlation between in vitro activity and binding affinity. © Pleiades Publishing, Ltd. 2026.

We investigate neutron star matter with hyperons within a density-dependent relativistic mean-field framework using Bayesian inference, considering three composition scenarios: purely nucleonic matter, hyperonic matter under SU(6) flavor symmetry, and hyperonic matter under SU(3) symmetry with free vector-sector parameters. The analysis incorporates constraints from empirical nuclear matter properties, theoretical inputs at low densities, and multimessenger observations of neutron stars. We find that the SU(6) scheme, grounded in the quark model and isospin counting rule, leads to a significantly softer equation of state. In contrast, the additional flexibility of the SU(3) framework enhances vector repulsion and yields a comparatively stiffer equation of state consistent with observational bounds across the explored parameter space; in particular, the posterior distributions favor values of the vector coupling ratio αv lower than the SU(6) limit αv = 1. These differences are reflected in neutron star observables, including mass-radius relations, tidal deformabilities, direct Urca thresholds, and oscillation properties, all of which remain compatible with current constraints within the SU(3) scenario. We further examine structural signatures through the curvature of the mass-radius relation and find that, although hyperon-rich configurations can induce noticeable variations, such features depend sensitively on the stiffness of the equation of state and are therefore not universally robust indicators. Bayesian model comparison further shows that present constraints do not meaningfully discriminate between the purely nucleonic and SU(3) hyperonic scenarios, while providing positive, but not decisive, evidence against the more restrictive SU(6) framework. Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.

The integration of Large Language Models (LLMs) into healthcare is poised to revolutionize various aspects of medical practice, including clinical decision-making, patient care, and medical research. This review explores the applications of LLMs such as ChatGPT-3, ChatGPT-4, and BERT in healthcare, focusing on their potential to enhance disease diagnosis, treatment planning, and personalized care. The paper presents a comprehensive bibliometric analysis of the growing body of research, highlighting key trends, influential authors, institutions, and geographical contributions. Despite their promise, significant challenges remain, including model accuracy, data privacy, ethical concerns, and the need for domain-specific fine-tuning. This review examines the moral and technical challenges associated with deploying LLMs in healthcare, including biases, a lack of transparency, and issues related to model interpretability. The paper further emphasizes the importance of robust frameworks for ensuring ethical usage. It proposes future research directions to address these challenges, including the development of specialized healthcare models, enhanced transparency, and improved integration into clinical workflows. Ultimately, this review aims to inform healthcare professionals, researchers, and policymakers about the transformative potential of LLMs in healthcare while underscoring the critical issues that must be overcome for their widespread adoption. This article is categorized under: Application Areas > Health Care Fundamental Concepts of Data and Knowledge > Big Data Mining Technologies > Artificial Intelligence. © 2026 Wiley Periodicals LLC.

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.