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Updated: Jun 23, 2026

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Nondestructive Localization of Subvisual Defects in Laser-Induced Graphene via Machine-Learning-Assisted Electrical

Keiya Minakawa1, Kotaro Takanashi1, Yuki Kimura1

  • 1Department of Applied Electronics, Graduate School of Advanced Engineering, Tokyo University of Science, Katsushika, Tokyo 125-8585, Japan.

ACS Omega
|June 22, 2026
PubMed
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This summary is machine-generated.

Machine-learning-assisted electrical resistance tomography (ML-ERT) detects hidden defects in laser-induced graphene (LIG) invisible to traditional methods. This non-destructive technique precisely locates functional degradations, ensuring quality control for carbon electronics.

Area of Science:

  • Materials Science
  • Electrical Engineering
  • Non-destructive Testing

Background:

  • Conventional imaging struggles to detect functional degradations without visible structural changes.
  • Porous laser-induced graphene (LIG) is susceptible to subvisual defects impacting performance.
  • Quality control for large-area carbon electronics requires advanced defect detection methods.

Purpose of the Study:

  • To develop and validate a machine-learning-assisted electrical resistance tomography (ML-ERT) framework.
  • To demonstrate ML-ERT's capability in localizing optically invisible defects in LIG.
  • To establish ML-ERT as a sensitive diagnostic tool for carbon electronics.

Main Methods:

  • Introduction of "subvisual" defects in LIG using masked O2 plasma irradiation.

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  • Application of machine learning-assisted electrical resistance tomography (ML-ERT) with a 1D CNN inverse solver.
  • Utilizing 3D finite element analysis to understand defect mechanisms.

    • Main Results:

    • ML-ERT successfully located defects with resistance surges up to 4 orders of magnitude.
    • Hidden failures were pinpointed when resistance contrast reached R/R0 ≥ 6.71.
    • Defect localization correlated with >30% effective conductive volume loss, indicating internal pathway degradation.

    Conclusions:

    • ML-ERT is a robust, non-destructive modality for detecting electrically critical but optically invisible failures in LIG.
    • The developed ML-ERT framework offers high sensitivity for quality control of carbon electronics.
    • This technique addresses the limitations of conventional imaging for functional defect assessment.