ArticleScientific reports2025
Mapping defect distribution in transparent single-walled carbon nanotube film with electrical resistance tomography.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The trial behind it
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Who cites it
2 citing papers in PubMed.
- Irradiation-Induced Structural Evolution and Functional Applications of Carbon-Based Materials: A Review.Nanomaterials (Basel, Switzerland) · 2026Review
- Nondestructive Localization of Subvisual Defects in Laser-Induced Graphene via Machine-Learning-Assisted Electrical Resistance Tomography.ACS omega · 2026Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
We extend the application of electrical resistance tomography (ERT) to visualize IG/ID distribution in transparent single-walled carbon nanotube (SWCNT) thin films. By establishing the correlation between disorder and resistance through experimental measurements, we successfully converted ERT-reconstructed conductivity distributions into IG/ID maps. This approach enabled the detection of a defect region with an IG/ID ratio of approximately 15 embedded in a pristine SWCNT film (IG/ID = 18.1), corresponding to a minimum detectable resistance ratio (R/R0) of 1.59. Moreover, in a sample containing two defect regions introduced by plasma irradiation at 20 and 40 W, ERT successfully distinguished them despite the small difference in IG/ID ratio (1.1). Simulation studies revealed that positional deviations in reconstructed images can be attributed to electrode misalignment, contact resistance variation, and plasma-induced defect expansion. These results demonstrate the feasibility of ERT as a non-destructive, high-resolution technique for evaluating disorder variations in conductive thin films. Furthermore, due to its conductivity-based imaging principle, the method is applicable to other materials such as graphene, indium tin oxide (ITO), and metal nanowire networks, offering promise for real-time monitoring and quality assurance in large-area optoelectronic device manufacturing.
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Registered trials
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