Epoxy vitrimer based nanocomposites for EMI shielding: Integrating reprocessability, self-healing, and machine learning predictions

• Epoxy vitrimer composite with MWCNTs and CoFe 2 O 4 developed for EMI shielding • Disulfide bond of vitrimer demonstrated self-healing and reprocessability • Epoxy vitrimer showed electrical percolation threshold at 0.2 wt% MWCNTs • Optimal EMI shielding of −18.4 dB with 15 wt% CoFe 2 O 4 and 0.2 wt% MWCNTs • Machine and deep learning models predict EMI shielding and mechanical Properties The rapid evolution of electronic technologies necessitates multifunctional polymeric materials capable of offering robust electromagnetic interference (EMI) shielding, with mechanical durability, reprocessability and self-healing. The investigation utilized, epoxy vitrimer nanocomposites reinforced with conductive multi-walled carbon nanotubes (MWCNTs) and magnetic cobalt ferrite (CoFe) nanoparticles to engineer high-performance, reprocessable EMI shielding materials. The vitrimer matrix, synthesized using diglycidyl ether of bisphenol A (DGEBA) and 4-aminophenyl disulfide (4-AFD), was optimized to incorporate dynamic covalent disulfide bonds enabling thermal reprocessability, self-healing, and solvent resistance. A fixed MWCNTs content of 0.2 wt% established as the electrical percolation threshold was used to ensure a conductive network, while CoFe content was systematically varied from 0 to 20 wt%. The synergistic interplay between the conductive and magnetic fillers resulted in an optimal shielding effectiveness (SE T ) of -18.4 dB at 0.2 wt% MWCNTs and 15 wt% CoFe, primarily governed by absorption losses due to interfacial polarisation and multiple scattering. Mechanical and thermal analyses demonstrated a balanced trade-off between stiffness and toughness, with enhanced thermal stability, and reprocessability. Furthermore, machine learning (Random Forest) and deep learning (DNN with Adam optimiser) models were deployed to accurately predict the tensile properties and shielding effectiveness, offering a data-driven framework for design optimisation. The developed vitrimer composites also exhibited excellent solvent resistance and self-healing capability, underscoring their potential for sustainable, high-performance EMI shielding applications in aerospace, electronics, and defence industries.

Authors

Institutions

Publication Details

Journal
Composites Part C Open Access
Published
2026-02-26
DOI
https://doi.org/10.1016/j.jcomc.2026.100712
Primary Topic
Electromagnetic wave absorption materials
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Epoxy vitrimer based nanocomposites for EMI shielding: Integrating reprocessability, self-healing, and machine learning predictions

Jerrin Joy Varughese, Arun Sam Varghese, L. Thomas, Sampath Parasuram et al.
Composites Part C Open Access
Electromagnetic wave absorption materials
article

Epoxy vitrimer based nanocomposites for EMI shielding: Integrating reprocessability, self-healing, and machine learning predictions

Jerrin Joy Varughese, Arun Sam Varghese, L. Thomas, Sampath Parasuram, Suryasarathi Bose, M.S. Sreekanth, T.P. Sumangala, S. Manobalan
article en

Abstract

• Epoxy vitrimer composite with MWCNTs and CoFe 2 O 4 developed for EMI shielding • Disulfide bond of vitrimer demonstrated self-healing and reprocessability • Epoxy vitrimer showed electrical percolation threshold at 0.2 wt% MWCNTs • Optimal EMI shielding of −18.4 dB with 15 wt% CoFe 2 O 4 and 0.2 wt% MWCNTs • Machine and deep learning models predict EMI shielding and mechanical Properties The rapid evolution of electronic technologies necessitates multifunctional polymeric materials capable of offering robust electromagnetic interference (EMI) shielding, with mechanical durability, reprocessability and self-healing. The investigation utilized, epoxy vitrimer nanocomposites reinforced with conductive multi-walled carbon nanotubes (MWCNTs) and magnetic cobalt ferrite (CoFe) nanoparticles to engineer high-performance, reprocessable EMI shielding materials. The vitrimer matrix, synthesized using diglycidyl ether of bisphenol A (DGEBA) and 4-aminophenyl disulfide (4-AFD), was optimized to incorporate dynamic covalent disulfide bonds enabling thermal reprocessability, self-healing, and solvent resistance. A fixed MWCNTs content of 0.2 wt% established as the electrical percolation threshold was used to ensure a conductive network, while CoFe content was systematically varied from 0 to 20 wt%. The synergistic interplay between the conductive and magnetic fillers resulted in an optimal shielding effectiveness (SE T ) of -18.4 dB at 0.2 wt% MWCNTs and 15 wt% CoFe, primarily governed by absorption losses due to interfacial polarisation and multiple scattering. Mechanical and thermal analyses demonstrated a balanced trade-off between stiffness and toughness, with enhanced thermal stability, and reprocessability. Furthermore, machine learning (Random Forest) and deep learning (DNN with Adam optimiser) models were deployed to accurately predict the tensile properties and shielding effectiveness, offering a data-driven framework for design optimisation. The developed vitrimer composites also exhibited excellent solvent resistance and self-healing capability, underscoring their potential for sustainable, high-performance EMI shielding applications in aerospace, electronics, and defence industries.

Composites Part C Open AccessVol. 19
Providence College (US), T. D. Medical College (IN), APJ Abdul Kalam Technological University (IN), Indian Institute of Science Bangalore (IN), Vellore Institute of Technology University (IN)
Openalex Percentile: Top 44%
Electromagnetic wave absorption materials
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.