Implement Explainable Machine Learning to Improve Conductivity in Polymer-CNT Nanocomposites: Supporting Adaptive, Flexible, and Long-Lasting IoT Wrap-Around Electronics Applications

Year : 2026 | Volume : 14 | Special Issue 01 | Page : 238 254
    By

    M. Muthupandi,

  • M. Sukanya,

  • C.L. Annapoorani,

  • W. Nancy,

  • Jinugu Ranjith,

  • Murali Krishna Atmakuri,

  • D. Marichamy,

  1. Assistant Professor, Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi Chennai, Tamil Nadu, India
  2. Associate Professor, Department of CSE, Kathir College of Engineering, Coimbatore, Tamil Nadu, India
  3. Assistant Professor, Department of BME, Chennai Institute of Technology, Nandhambakkam, Kundrathur, Chennai, Tamil Nadu, India
  4. Assistant Professor, Department of ECE Jeppiaar Institute of Technology, Kanchipuram, Chennai, Tamil Nadu, India
  5. Assistant Professor, Department of Computer Science and Engineering, CMR College of Engineering & Technology Hyderabad, Telangana, India
  6. Assistant Professor, Department of Electronics and Communication Engineering, RVR &JC College of Engineering, Chowdavaram, Guntur, Andhra Pradesh, India
  7. Assistant Professor, Department of Artificial intelligence and Data Science, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai, Tamil Nadu, India

Abstract

The rapid growth of Internet of Things (IoT) technologies requires electronic components that are adaptable, lightweight, and durable, and that can continue to function well in diverse contexts and circumstances. Polymer–carbon nanotube (CNT) nanocomposites have become interesting choices for these kinds of uses because they are more flexible, conduct electricity better, and can be made to fit specific needs. However, improving conductivity in these heterogeneous systems remains a major challenge because of the complex relationships between polymer form, CNT dispersion, interfacial interactions, and processing conditions. This study introduces an explainable machine learning (XML) framework designed to systematically model and enhance conductivity in polymer-CNT nanocomposites, ensuring transparency and interpretability in predictions. The methodology integrates feature attribution techniques with interpretable model architectures to elucidate critical attributes, such as CNT concentration, aspect ratio, polymer crystallinity, and filler alignment, that significantly influence charge transport pathways. The architecture makes it possible to use adaptive tuning strategies to attain the optimum conductivity without giving up flexibility, durability, or ease of fabrication. To test the results, conductivity tests are taken on different compositions and processing methods. To help with material development, model predictions are employed. The knowledge gained helps to make polymer-CNT nanocomposite systems that are flexible, energy-efficient, and long-lasting for wrap-around IoT electronics, which need to be able to bend, stretch, and change with the environment all the time. Adding explainability not only makes things work better, but it also fosters trust, speeds up the search for new materials, and gives a plan for using this method on more multifunctional nanocomposites. The approach ultimately advances next-generation, sustainable electronic materials by combining data-driven optimization with basic physical laws

Keywords: Electrical Conductivity Optimization, Flexible Electronics, Adaptive Optimization Framework, Material Design Interpretability, Long-Term Durability, Polymer–Carbon Nanotube Nanocomposites, IoT Wrap-Around Devices.

[This article belongs to Special Issue under section in Journal of Polymer & Composites (jopc)]

How to cite this article:
M. Muthupandi, M. Sukanya, C.L. Annapoorani, W. Nancy, Jinugu Ranjith, Murali Krishna Atmakuri, D. Marichamy. Implement Explainable Machine Learning to Improve Conductivity in Polymer-CNT Nanocomposites: Supporting Adaptive, Flexible, and Long-Lasting IoT Wrap-Around Electronics Applications. Journal of Polymer & Composites. 2026; 14(01):238-254.
How to cite this URL:
M. Muthupandi, M. Sukanya, C.L. Annapoorani, W. Nancy, Jinugu Ranjith, Murali Krishna Atmakuri, D. Marichamy. Implement Explainable Machine Learning to Improve Conductivity in Polymer-CNT Nanocomposites: Supporting Adaptive, Flexible, and Long-Lasting IoT Wrap-Around Electronics Applications. Journal of Polymer & Composites. 2026; 14(01):238-254. Available from: https://journals.stmjournals.com/jopc/article=2026/view=239397


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Special Issue Subscription Original Research
Volume 14
Special Issue 01
Received 25/08/2025
Accepted 01/11/2025
Published 13/02/2026
Publication Time 172 Days
39

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