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

3 publications

  • Published Subscription Original Research

    Triboelectric Charge Density Enhancement in Zinc Oxide Decorated Polymer Nanogenerators for Energy Harvesting Systems

    By P. William, Dipesh B. Pardeshi, Atul A. Barhate, Abhishek Badholia, Sharad Ninu Kolte, Sanjay Dattatray Jondhale, Amit V. Mohod

    Abstract: The growing need for wearable electronics, wireless sensor networks, and IoT devices has pushed research on triboelectric nanogenerator for long-term energy harvesting. However, traditional polymer-based TENGs' low triboelectric charge density restricts their energy conversion efficiency and practical performance. This research presents a zinc oxide (ZnO)-decorated polymer triboelectric nanogenerator designed to enhance surface charge density and improve electrical output. ZnO nanoparticles were synthesized using a hydrothermal method and uniformly deposited onto …

    Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article

  • Published Subscription Original Research

    Machine Learning-Based Structure–Property Quantification of Advanced Polymer Composites

    By Ganesh P. Dawange, Bhushan S. Chaudhari, P. William, Atul A. Barhate, Sharad Ninu Kolte, Amit V. Mohod, Dipesh B. Pardeshi

    Abstract: Advanced polymer composites are widely used in high-performance engineering due to their superior mechanical and multifunctional properties. Accurate structure–property quantification is essential for efficient material design and reducing experimental costs. Existing Machine Learning (ML) approaches often exhibit limited predictive generalization due to inadequate feature discrimination and suboptimal hyperparameter tuning. To address these limitations, the proposed method enhances the ability to capture the complex nonlinear interactions among composite structural descriptors. The …

    Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article

  • Published Subscription Original Research

    Artificial Intelligence and Constitutive Modeling Equations for Predictive Design of High-Performance Polymer Composites

    By Harshal P. Varade, P. William, Amit V. Mohod, Dipesh B. Pardeshi, Atul A. Barhate, Sharad Ninu Kolte, Ganesh P. Dawange

    Abstract: Growing polymer composite applications demand accurate mechanical prediction, yet complex interactions and conventional constitutive models limit predictive capability and require extensive calibration. To report these challenges, this research recommends a combined Artificial Intelligence (AI) and constitutive modeling approach based on an Enhanced Tasmanian Devil Optimizer-tuned Residual Neural Network with Multilayer Perceptron (ETDO-ResNet-MLP) for the predictive design of high-performance polymer composites. The study uses a publicly available Polymer Composite Property Dataset …

    Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article

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