Effect of water immersion, laundering, and abrasion on the conductivity of reduced graphene oxide coatings on aramid fabrics
Bibliographic record
Abstract
Abstract Opportunities for developing end-of-life sensors for fire resistant fabrics are explored using reduced graphene oxide coatings on textiles. Fire resistant fabrics are known to experience significant losses in performance over time. Large reductions in mechanical properties have also been recorded when these fabrics were subjected to accelerated aging conditions simulating the use in service. In addition, the fabric loss in performance may exceed the safety requirement threshold before any sign of damage is visible to the naked eye. Electrically conductive coatings and tracks were prepared on an m-aramid woven fabric using graphene oxide that was further reduced. The preparation technique allowed wrapping the individual aramid fibers with rGO sheets. No significant change in sheet resistance was recorded after up to 120h of immersion of the rGO-coated fabric specimens in water. An increase in resistance after 10 accelerated washing cycles was measured on the rGO-coated specimens prepared with 5 coating cycles while no significant effect was detected for specimens prepared with 10 and 15 coating cycles. Under abrasion exposure, the electrical resistance of rGO tracks increased gradually until 150 cycles, after which the conductivity dropped abruptly. These results show the potential of reduced graphene oxide applied as a coating on m-aramid fabrics to prepare end-of-life sensors for fire resistant fabrics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".