STM Journals Peer-reviewed research from Consortium e-Learning Network

3 publications

  • Published Subscription Original Research

    Predicting Dielectric Constants of Polymers Using Molecular Structural Descriptors and Explainable Machine Learning: A Data-Driven Approach

    By C. Sridhathan, R. Arangasamy, Geetha Prahalad, Mohandass G., N.M.G. Kumar, M. Ram Prasad Reddy

    Abstract: Accurate prediction of dielectric constants in polymeric materials is fundamental to the rational design of advanced electronic components, energy storage capacitors, flexible substrates, and high-frequency communication circuits. Conventional approaches to identifying suitable polymer dielectrics rely on extensive experimental synthesis and characterisation, which are both time-consuming and resource-intensive. In this work, an explainable machine learning framework is developed to predict the dielectric constant of polymers directly from molecular structural descriptors derived …

    Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 297–304 Read article

  • Published Subscription Original Research Special issue

    Image-Based Quantitative Mapping of Structure Property Relationships in Polymer Composite Materials

    By Mohandass G., B. Ram Priya, R. Arangasamy, C. Sridhathan

    Abstract: The performance of polymer composite materials is intrinsically governed by their microstructural architecture, which is shaped by manufacturing conditions and constituent interactions. Despite extensive experimental characterization efforts, establishing transparent and quantitative structure–property relationships from microstructural images remains a challenge. In this study, an explainable image-driven framework is developed to systematically correlate microstructural features with composite property indicators. Microstructure images are processed to identify voids, fibers, and filler phases, from which …

    Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 188–196 Read article

  • Published Subscription Original Research Special issue

    Non-Contact Quantification of Swelling-Induced Deformation in Polymer Hydrogels Using Image Analysis

    By R. Arangasamy, C. Sridhathan, Mohandass G., B. Ram Priya, N.M.G. Kumar, D. Harika

    Abstract: Swelling of polymer hydrogels governs transport, mechanics, and functional performance in biomedical systems, yet it is often reported using bulk ratios that conceal spatially heterogeneous deformation and boundary-driven instabilities. This study presents a non-contact image-analysis framework to quantify swelling-induced deformation by tracking shape and boundary evolution from time-lapse imaging. The approach segments the hydrogel region, extracts a sub-pixel refined contour, and computes boundary displacement descriptors including mean and upper-percentile normal …

    Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 1499–1509 Read article

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