JSS Science and Technology University

Among a plethora of tactics, the electrochemical CO2-reduction method (ECO2RR) is a promising pathway to mitigate CO2 emissions and generate value-added chemicals simultaneously. Herein, a bimetallic NiO-HfO2-1 heterojunction catalyst was designed via the co-precipitation method, where the interfacial combination of NiO and HfO2 yields the remarkable synergistic effect due to strengthened interaction. The intrinsic structural and electronic properties were characterised using spectroscopic and analytical techniques. The as-designed catalyst was used as an electrode material for electrochemical analysis. NiO-HfO2-1 catalyst demonstrated a high current density (-33.68 mAcm- 2), a low overpotential and a low Tafel slope (83.38 mV dec-1), indicating that charge transfer through the electrode surface. An M-S plot gave evidence for the n-type semiconducting nature and the position of the band edges for the reduction of CO2. The catalyst was selective for formic acid with a Faradaic efficiency of over 96.08% in a long-term electrolysis test. The DFT computations promoted the experimental studies by showing that the NiO-HfO2-1 heterojunction interface is adept at lowering the energy barrier for CO2 activation. The combination of experimental electrochemical analysis, and DFT validation, which is used here to map the mechanistic route, reveals an intriguing catalyst design for sustainable energy conversion.

An eco-friendly and efficient route for synthesizing reduced graphene oxide (rGO) using Mimosa pudica leaf extract as a green reducing and stabilizing agent is reported. To address the limited understanding of structure–property relationships in green-synthesized rGO, a systematic study is performed. Analysis of graphene oxide (GO) and rGO using XRD, FTIR, Raman, and XPS confirms effective deoxygenation and restoration of sp2 domains. The spin-coated thin films of Mimosa pudica leaf extract reduced graphene oxide (MP-rGO) exhibit increased conductivity with a sheet resistance (Rs) of 373 ± 18 kΩsq−1 and high optical transmittance of 93.61

Reliable condition monitoring of rolling element bearings is essential for improving machinery availability and reducing unplanned downtime. Machine learning (ML) techniques have been widely applied for vibration-based bearing fault diagnosis; however, reported performance is often difficult to interpret due to variations in datasets, operating conditions, and validation protocols. This study presents a controlled experimental benchmark evaluating three classical ML algorithms—Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), and Artificial Neural Network (ANN)—for vibration-based fault classification of cylindrical roller bearings. Experiments were conducted on SKF N204 bearings using a machinery fault simulator under three bearing conditions (healthy, outer race defect, and roller defect) at four rotational speeds (100–400 RPM). Artificial defects were introduced using electrical discharge machining (EDM), and vibration signals were acquired using a National Instruments data acquisition system. Root mean square (RMS), kurtosis, and crest factor were extracted as interpretable statistical features, resulting in a dataset of 119 feature samples. Model performance was evaluated using stratified train–validation–test splits, with k-fold cross-validation applied uniformly across all classifiers, including the ANN, to ensure fair comparison. Under the present laboratory conditions, the ANN exhibited the most consistent performance across validation and testing, while SVM and k-NN showed slightly lower validation accuracy but comparable testing behavior. The results demonstrate that high classification accuracy can be achieved using simple statistical features and classical ML models in a controlled setting. However, the findings should be interpreted as upper-bound diagnostic performance under laboratory conditions, rather than direct indicators of industrial-scale generalization. The study provides a transparent benchmarking reference and highlights the trade-off between interpretability, data efficiency, and classification performance in bearing health monitoring applications.

Particle-fluid interactions have a major impact on heat transmission, thermal efficiency, and wall-cooling efficacy in combustion chambers, gas turbines, coal-fired boilers, and plasma devices. The potential of dusty fluids to enhance heat and mass transport in intricate biological and technological contexts has generated a lot of attention. A dusty fluid flow is crucial in several industrial and technical applications where solid particles interact thermally and dynamically with a base fluid. Based on a comprehensive and application-focused synthesis of peer-reviewed publications from the Scopus database, the current study offers a critical assessment of recent advancements in the study of dusty fluids and dusty nanofluids. A thorough review of the selected studies was carried out in order to assess the effects of advanced thermal processes, particle dynamics, non-Newtonian rheology, and multiphase interactions on transport parameters. The work highlights the need for experimental validation and particle behaviour modelling, particularly for dusty ternary and tetra-hybrid nanofluids, although the fact that theoretical and numerical investigations prevail in the literature. Microelectronics cooling, biological fluxes, renewable energy systems, aircraft thermal management, and environmental transport processes are among the real-world applications discussed. The research also demonstrates that, to develop energy-efficient, application-ready thermal systems, entropy generation studies, optimisation techniques, and data-driven methods must be combined. To transform dusty nanofluid technology into practical engineering solutions, the present study highlights significant research gaps and new opportunities while providing a structured framework to direct future experimental, computational, and design-focused research efforts.

A significant topic in transport problems emerges when a flow’s characteristic length scale approaches the granular or molecular dimensions of the medium. The use of micropolar fluid theory, which considers the particle rotation and coupling stresses that Newtonian models ignore is motivated in such situations by the importance of the intrinsic rotation and microstructural motion of the material constituents. Applications in thermal engineering, biological transport, lubrication, energy systems and polymer manufacturing all depend on an understanding of heat transfer in micropolar fluids. Recent developments in the heat transfer behaviour of micro-structured fluids under the impact of internal heat generation or absorption, thermal radiation, chemical reactions, Soret-Dufour effects, viscous dissipation, magnetic field and porous media are systematically examined in this study. Using major scientific databases, a PRISMA-guided literature assessment was carried out with an emphasis on peer-reviewed research that includes at least one multi-physics interaction and was published between 2021 and 2025. Excluded studies lacked micro-structured fluid formulations or thermal analysis. Significant obstacles still exist despite significant advancements, such as simplified material characteristics, little experimental confirmation and limited handling of intricate geometries and three-dimensional effects. By considering all these aspects, this review highlights important research gaps and offers a cohesive framework for comprehending multi-physics heat transfer in micropolar fluids. For researchers and engineers working on microfluidic systems, sophisticated thermal modelling and energy-related applications utilizing micro-structured fluids, the current review meant to act as a guide and roadmap.