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

3 publications

  • Published Subscription Original Research Special issue

    Machine Learning-Based Quantification of Polymer Structure Property Relationships for Predictive Material Design

    By Kalpana G. Joshi, Shanthi Kumaraguru, Prashant M. Yawalkar, P. William, Prashant V. Thokal, Jaikumar M. Patil

    Abstract: Polymer structures exhibit complex, hierarchical arrangements that strongly influence macroscopic properties, yet consistent quantification remains challenging due to nonlinear interactions and limited unified modeling strategies. Existing approaches inadequately capture generalized structure–property mappings across diverse polymer systems. This research aims to establish a machine learning-based quantification model for polymer structure–property relationships to support predictive material design. A Polymer Structure Property Dataset of 5,000 polymer samples includes structural descriptors and experimentally measured …

    Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 737–754 Read article

  • Published Subscription Original Research Special issue

    High-Performance Polymer Composite Membranes for Selective Ion Transport in Advanced Energy Conversion Systems

    By Shanthi Kumaraguru, Prashant V. Thokal, P. William, Ganesh P. Dawange, Dharmendra Kumar Roy, Pravin B. Khatkale

    Abstract: The development of high-performance Polymer Composite Membranes (PCM) with enhanced ion transport is essential for Polymer Electrolyte Membrane Fuel Cells (PEMFC). However, low-humidity conditions and filter agglomeration still limit long-term membrane stability. This research designs a functionalized PCM integrating cross-linked Poly (Vinyl Alcohol)/Poly (Ethylene Glycol) (PVA/PEG) matrix with sulfonic acid functional groups and Titanium Dioxide (TiO₂) nanofillers to improve selective ion transport under low-humidity conditions. Two types of PCM were …

    Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 724–736 Read article

  • Published Subscription Original Research

    Data-Driven Digital Twin Model for Real-Time Strength Estimation in Polymeric Materials

    By Makrand V. Kulkarni, Kalpana G. Joshi, Shanthi Kumaraguru, Vinit Kotak, Prashant V. Thokal, Prashant M. Yawalkar, Vijay More

    Abstract: The real-time prediction of mechanical properties in polymeric materials is essential for ensuring quality, consistency, and operational efficiency in modern manufacturing systems. As industrial processes become increasingly complex, traditional trial-and-error approaches to material characterization are no longer sufficient to meet the demands of high-throughput production environments. This study introduces a digital twin-integrated machine learning approach for the real-time estimation of tensile strength in polymeric materials by combining simulation-driven insights with …

    Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 246–257 Read article

Support