7 publications
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Published Subscription Original Research Special issue
Machine Learning Based Optimization of Polymer Structure Property Relationships in Composite Material SystemsBy Prashant V. Thokal, P. William, Ganesh P. Dawange, Dharmendra Kumar Roy, Pravin B. Khatkale
Abstract: In modern engineering applications, polymer-based composite materials have garnered a lot of attention because of their lightweight nature, high strength-to-weight ratio, and changing physical features. In order to maximize the relationships between polymer structure and properties in composite materials, this study suggests a strategy based on reinforcement learning (RL). The research utilized the Polymer Composite Properties Dataset, which contains 12,700 records associated with polymer matrices, reinforcement fillers, interfacial bonding characteristics, …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 242–255 Read article →
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Published Subscription Original Research Special issue
Catalytic Performance Improvement in Polymer Derived Nanostructures for Fuel Cell ApplicationsBy Prasad M. Patare, Ganesh P. Dawange, P. William, Dharmendra Kumar Roy, Pravin B. Khatkale
Abstract: The performance and operational lifetime of PEMFCs are closely linked to the catalytic activity and durability of cathode electrocatalysts. In this research, a polymer-derived cobalt–iron nitrogen-doped carbon (CoFe–N/C) nanostructured catalyst is developed to enhance the ORR performance for PEMFC applications. PAN and melamine serve as nitrogen-rich polymer precursors, while cobalt and iron salts generate highly dispersed metal–nitrogen active sites within a porous carbon matrix through thermal treatment at 900 °C. …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 968–979 Read article →
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Published Subscription Original Research Special issue
Machine Learning-Based Quantification of Polymer Structure Property Relationships for Predictive Material DesignBy 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 →
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Published Subscription Original Research Special issue
High-Performance Polymer Composite Membranes for Selective Ion Transport in Advanced Energy Conversion SystemsBy 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 →
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Published Subscription Original Research
An Innovative AI-Integrated Approach for Identifying the Tensile Robustness of Polymeric MaterialsBy M.V. Kulkarni, P. William, Pravin B Khatkale, Sandip R. Thorat, Santosh Kumar Sharma, Apurv Verma
Abstract: Polymeric materials have so many applications and character similar to flexibility, robustness and lightweight nature they are essential to a large variety of industries. Though, it is difficult to establish their tensile robustness appropriately, particularly in a variety of environmental situation. Provide a recommended Artificial Intelligence (AI)-integrated method to decide the issues of rapidly ascertaining the tensile robustness of the polymeric material. Using machine learning (ML), this study, predicted and …
Published in Journal of Polymer & Composites · Vol. 13, Issue 1, 2025 · pp. 90–97 Read article →
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Published Subscription Review Article
Developing Food Packaging with Nanomaterials: In-Depth Examination based on Electromagnetic and Optical RadiationsBy P. William, Pravin B. Khatkale, Harshal P. Varade, Santosh Kumar Sharma, N. Yogeesh
Abstract: Problems with food degradation, infection, and environmental compatibility are driving constant innovation in the food packing sector. Nanomaterials (NM) are investigated as possible solutions to the problem to improve the effectiveness of food packaging by means of their exceptional optical and electromagnetic characteristics. Several factors, such as the need to analyze capacity, cost-effectiveness, and possible environmental implications, prohibit the use of NM in food packing. This review summarizes the optical …
Published in Journal of Polymer & Composites · Vol. 12, Issue 6, 2024 · pp. 36–44 Read article →
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Published Subscription Original Research
Developing an AI-Based Novel Forecasting Framework for Surface Irregularity in Metal Matrix MaterialsBy M.V. Kulkarni1, P. William, Pravin B Khatkale, Harshal P. Varade, Sandip R. Thorat
Abstract: Surface irregularity in metal matrix materials (MMM) signifies the deviations from smoothness, influencing structural integrity and performance frequently arising from the manufacturing process along with intrinsic material characteristics that influence effectiveness. Limitations in data, model interpretability and complexity are the difficulties that impede artificial intelligence (AI) based surface irregularity in MMM. In this study, we suggested a novel framework of Gaussian regression fused multi-strategy adaptive boosting classifier (GR-MABC) for the …
Published in Journal of Polymer & Composites · Vol. 12, Issue 5, 2024 · pp. 48–56 Read article →