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 Evaluation of Implant Tissue Interface Integrity in Polymer Orthopaedic Devices

    By Sheeba Santhosh, S.A. Yuvaraj, Chengamma Chitteti, NMG Kumar, M. Ram Prasad Reddy

    Abstract: Polymer orthopedic implants offer radiolucency and mechanical compatibility with bone, but long-term success depends on maintaining a stable implant–tissue interface. Routine imaging is widely available for follow-up, yet interface integrity is commonly judged qualitatively, limiting early detection of fixation compromise and reducing comparability across devices and time points. This work presents an image-based methodology to quantify interface integrity by extracting interpretable interface descriptors from a standardized interface belt around the …

    Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 170–179 Read article

  • Published Subscription Original Research Special issue

    Adaptive Drift Correction in Polymer-Based Wearable Biosensors via Data-Driven Signal Modeling

    By A. Sabari Vani, Sheeba Santhosh, Geetha Prahalad, M. Ram Prasad Reddy

    Abstract: Polymer-based wearable biosensors have emerged as a promising technology for continuous health monitoring due to their mechanical flexibility, biocompatibility, and suitability for long-term physiological interfacing. However, prolonged exposure to biofluids, environmental variability, and mechanical deformation introduces signal drift, which significantly degrades measurement accuracy and limits clinical reliability. This paper presents a data-driven methodology for compensating signal drift in polymer-based wearable biosensors using adaptive signal processing and machine learning techniques. The …

    Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 131–139 Read article

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