Siksha 'O' Anusandhan

The investigation of temperature-induced and heat-driven diffusion effects on the off-centered stagnation point flow (OSF) of non-Newtonian fluid (NNF) across a rotating disk (RD) has considerable applications in industries, including concurrent heat and mass transfer. The applications include polymer extrusion, chemical vapor deposition, petroleum refining, and heat management systems using NNFs. Inspired by this, the present work investigates the Dufour and Soret consequences on the OSF of Maxwell fluid via an RD. Additionally, the influence of thermophoresis and Brownian motion is considered to assess the mass and heat transport attributes. The governing differential equations are converted into ordinary differential equations by applying the appropriate similarity transformations. Furthermore, the reduced equations are solved numerically by employing the Runge-Kutta Fehlberg fourth-fifth-order approach. Moreover, the fluid's profile is evaluated using the artificial neural network technique. The significant outcomes of the study show that the rotation parameter reduces the azimuthal velocity while enhancing the radial velocity. The velocity profile declines as the Maxwell parameter rises. The thermal profile increases with higher values of the Dufour number, thermophoresis, and Brownian motion parameters. The rise in the thermophoresis parameter and the Soret number increases the concentration profile.

The widespread accumulation of macro- and microplastics in terrestrial and marine environments has emerged as a pressing global challenge due to their persistence, ecotoxicological effects, and disruption of biogeochemical processes. Conventional plastic waste management strategies remain inadequate, thereby driving interest in biodegradation as a sustainable alternative. This review critically evaluates microbial, enzymatic, and physicochemical mechanisms involved in the degradation of commonly used polymers, including polyethylene (PE), polypropylene (PP), polyethylene terephthalate (PET), and polystyrene (PS). Microorganisms such as Pseudomonas, Ideonella, and Aspergillus spp. have demonstrated promising degradation potential, mediated by enzymes such as PETase, cutinase, and laccase. The ecological implications of plastic fragmentation and its degradation byproducts on marine ecosystems, biodiversity, food webs, and human health are also highlighted, with particular attention to major plastic-emitting regions such as the Philippines, India, and China. Finally, the review discusses current limitations and future directions, including genetic engineering of plastic degraders, integration of biodegradation with circular bioeconomy frameworks, and the design of inherently biodegradable polymers. Addressing plastic pollution effectively will require an interdisciplinary strategy that integrates microbiology, materials science, and environmental policy.

This study examined the influence of TiO2 nanoparticles on the thermomechanical fatigue reliability of SAC305 solder joints, with the aim of linking their microstructural evolution, interfacial stability, and electrical performance degradation. Solder joints with and without 0.5 wt.

This study develops machine-learning models for predicting the solubility of Glibenclamide and the density of supercritical CO₂ under varying temperature and pressure conditions. Three regression techniques—Polynomial Kernel Ridge Regression (PKR), Weighted Least Squares (WLS), and Gradient Boosting Trees (GBT)—were employed, with hyperparameters optimized via the Rain Optimization Algorithm (ROA). PKR delivered the highest solubility-prediction accuracy, achieving an R2 of 0.98689, RMSE of 3.1884 × 10⁻1, MAE of 2.73613 × 10⁻1, and MAPE of 1.33900 × 10⁰. For density prediction, PKR also performed best, with an R2 of 0.98169, RMSE of 2.0935 × 101, MAE of 1.70231 × 101, and MAPE of 2.92063 × 10⁻2. GBT showed competitive performance (R2 = 0.93256 for solubility; 0.91889 for density), while WLS produced moderate accuracy. In comparison with previous studies that modeled Glibenclamide solubility using simpler machine-learning methods, the present work introduces an advanced PKR–ROA framework capable of accurately predicting both solubility and supercritical-fluid density. The proposed approach provides a practical computational tool for optimizing SC-CO₂-based pharmaceutical processing.

Developing advanced supercapacitor electrodes with both high energy density and long-term cycling stability is a critical challenge in energy storage. While metal oxides like zinc oxide (ZnO) offer high theoretical pseudocapacitance, they often suffer from poor electrical conductivity and structural degradation during cycling. To overcome these limitations, we designed and fabricated a novel, binder-free hierarchical composite, carbon-coated zinc oxide Nanorod@Aluminum Oxide Nanofiber (ZnO–NR@C/Al₂O₃–NF), where a stable alumina nanofiber scaffold and a conductive carbon coating work synergistically to enhance the performance of ZnO nanorods. The composite was synthesized via a multi-step approach. First, a robust Al₂O₃–NF scaffold was fabricated by electrospinning followed by high-temperature calcination. Next, ZnO–NR were grown directly onto the scaffold using a hydrothermal method. Finally, a uniform, porous carbon layer was coated onto the ZnO nanorods through hydrothermal carbonization of glucose and subsequent annealing. Comprehensive characterization confirmed the successful synthesis of a unique, hierarchical, and highly porous architecture. Further analyses using XRD and XPS verified the composite’s high purity, expected crystallographic phases, and crucial electronic interactions between the carbon coating and the metal oxides. Most significantly, the electrode demonstrates outstanding long-term durability, maintaining its structural and crystallographic integrity with minimal degradation after 8000 charge–discharge cycles. This exceptional stability is attributed to the synergistic design, where the Al₂O₃ scaffold provides mechanical support and the carbon coating enhances conductivity while preventing the pulverization of the active ZnO material, validating its potential for high-performance energy storage applications.