Mangalayatan University

We construct a new class of exact solutions for shear-free, spherically symmetric radiating stars undergoing gravitational collapse in the presence of pressure anisotropy. Starting with the static metric potentials, we specialise the parametric family to the inhomogeneous anisotropic subclass n=- 2 and carry out a complete dynamical and thermodynamic analysis of this configuration. The interior solution, obtained in closed form, satisfies the standard regularity and energy conditions (null, weak, strong, and dominant), and is matched across the stellar boundary to an exterior Vaidya spacetime through the appropriate junction conditions. The configuration admits an initially static perfect-fluid limit and evolves smoothly into a dissipative collapse driven by radiative heat transport. A numerical study is also performed, wherein the effect of the anisotropy parameter on radial profiles and temporal evolutions of the density, pressures, heat flux, collapse rate, redshift, luminosity, and interior temperature is studied. The energy conditions are verified. The results demonstrate that increasing pressure anisotropy strengthens the matter variables, accelerates the contraction rate and enhances thermal dissipation throughout the radiating phase.

Drug-resistant epilepsy (DRE) remains a major therapeutic challenge, affecting millions of patients globally who do not respond to conventional antiepileptic medications. Recent evidence highlights GABA transporter 3 (GAT3), a glial subtype responsible for regulating extracellular GABA levels, as a promising molecular target for seizure control. Despite its therapeutic potential, selective and potent GAT3 inhibitors remain largely unexplored. This study aims to identify novel GAT3 inhibitors through an integrative computational strategy combining quantum chemical, molecular dynamics, and machine learning approaches to accelerate antiepileptic drug discovery. The cryo-EM-resolved human GAT3-SNAP-5114 complex structure was used for virtual screening, leading to the identification of three highest-ranking compounds, viz. Comp_1, Comp_2, and Comp_3, from the diverse chemically optimized screening library. Analysis of their quantum chemistry profiles indicated HOMO-LUMO energy gap values of 4.14 eV, 4.21 eV, and 3.26 eV, respectively, suggesting them to be maximally reactive with favorable electronic properties for interacting with biological molecules. Molecular dynamics simulations were performed in a fully hydrated POPC lipid bilayer using the AMBER force field, providing a physiologically relevant environment for assessing ligand binding and transporter dynamic, revealed stable binding modes, with the highest structural integrity or least deviations in the structure with Comp_2, having RMSD values ranging from 2.5 to 3.0 Å, with strong hydrogen bonding, while the binding mode was more flexible with strong anchorage for Comp_3, with its mode involving diverse interactions in the complex. Principal component and free energy landscape analyses identified energetically favorable, ligand-induced conformational states. Machine learning–based pIC₅₀ predictions using the AdaBoost regressor yielded values of 7.95 (Comp_1), 7.98 (Comp_2), and 7.63 (Comp_3), all comparable or superior to the reference inhibitor (7.89). A novel approach for network pharmacology integrating detailed drug-gene interaction profiling was additionally used to improve systems-level understanding of the discovered compound. These findings validate the potential of Comp_1–3 as lead GAT3-targeting antiepileptic agents. A docking-based virtual screening of a structurally diverse compound library was performed against the cryo-EM structure of human GAT3 in complex with the reference inhibitor SNAP-5114. The top-scoring ligands were subjected to density functional theory (DFT) analysis to assess electronic properties such as HOMO-LUMO energy gaps, reflecting potential binding reactivity. Lead candidates were refined, docked, and evaluated through 500 ns molecular dynamics (MD) simulations, analyzing structural stability via RMSD, RMSF, hydrogen bonding, and interaction network profiles. Principal component analysis and free energy landscape evaluations were used to explore ligand-induced conformational dynamics. Furthermore, machine learning (ML) models—particularly the AdaBoost algorithm—were employed to predict pIC₅₀ values, confirming bioactivity trends. Finally, network pharmacology analysis was conducted to elucidate gene targets and pathway interconnections, revealing the multi-pathway involvement of top candidates in seizure regulation. This integrative computational framework underscores the potential of GAT3-focused modelling and ML-guided screening for rational antiepileptic drug design.

Polymer-ceramic hybrid composites are emerging as attractive candidates for lightweight, corrosion-resistant absorber components in solar thermal collectors; however, their adoption is constrained by the intrinsically low thermal conductivity of polymers, processing-induced anisotropic heat transport, interfacial thermal resistance at tube/laminate joints, and durability challenges under outdoor exposure. This review provides a collector-centered synthesis of polymer-ceramic hybrid materials, emphasizing the translation of composite properties into collector-level outcomes rather than conductivity enhancement alone. A structure-property-performance mapping approach is presented to connect directional thermal conductivity ((k_in-plane), (k_perp)), thermal diffusivity, heat capacity, coefficient of thermal expansion, and service temperature with collector performance parameters such as heat removal effectiveness, overall heat losses, and stagnation behavior. Ceramic fillers (e.g., boron nitride, aluminum nitride, silicon carbide, alumina) are examined for stable conduction-network formation, coating compatibility, and long-term reliability, while carbon fillers (graphite, graphene nanoplatelets, carbon nanotubes) are evaluated for combined heat spreading and solar absorption benefits, with attention to emissivity penalties. Hybrid ceramic-carbon architectures and multilayer absorber designs are identified as the most promising routes to balance thermal transport, optical selectivity (high solar absorptance and low thermal emittance), manufacturability, and durability under UV, humidity, and thermal cycling.

The development of multifunctional photocatalyst capable of simultaneously addressing environmental pollution and clean energy production is of great importance. In this work, sulfur-doped graphitic carbon nitride (S-CN) and Fe-doped ZnO (FZ) were first synthesized individually, followed by the fabrication of a series of S-CN/FZ composites with varying loadings (5, 10, 15, and 20 wt% of FZ). Notably, this designed dual-doped type-II heterojunction integrates photocatalytic oxidation, reduction, and hydrogen evolution within a single visible light responsive system. The structural, morphological, and optical properties of the prepared materials were systematically characterized using XRD, FTIR, XPS, FESEM-EDX, PL, and UV-Vis DRS, PL, and electrochemical analyses, confirming the successful formation of heterojunctions and enhanced visible-light absorption. Among all the composites, S-CN/15FZ exhibited superior photocatalytic activity, achieving rapid reduction of 4-nitro-phenol (96.3%) and efficient degradation of tetracycline hydrochloride (94.7%) under visible light irradiation. Furthermore, the optimized heterostructure demonstrated remarkable performance in photocatalytic H2evolu-tion (5402.91 mu mol g-1 h-1), surpassing that of the pristine components. The enhanced activity is attributed to the synergistic effects of S and Fe dual doping, which are responsible for tailoring the band structure and promoting the charge transfer across the S-CN/FZ interface. This study highlights the potential of S-CN/FZ heterostructures as promising candidates for multifunctional photocatalysis in environmental remediation and sustainable energy applications.