Electrical conduction of reduced graphene oxide coated meta-aramid textile and its evolution under aging conditions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
High-performance textiles – such as those used in protective clothing – age silently, undergoing a gradual reduction in their protective properties. We propose that an electrically conducting layer that loses its conductivity systematically under aging conditions can be used as an end-of-life-sensor for textiles. In the present work, we first present a simple method to prepare conductive tracks on a meta-aramid woven fabric using reduced graphene oxide. While 15 iterations of reduced graphene oxide coating cycles were needed to wrap around each m-aramid fiber with reduced graphene oxide sheets completely, 10 cycles were sufficient to establish the electrical conductivity that remained stable for up to 10 laboratory wash cycles (equivalent to 50 domestic laundry cycles). The conductivity of these reduced graphene oxide coated fabrics remained stable upon immersion in water. Furthermore, we established a fabrication protocol for patterning both single-sided and two-sided reduced graphene oxide tracks on the m-aramid fabric. The former is designed to lose its conductivity upon abrasion, while the latter is designed to undergo a gradual transition in properties during aging. Assessment with a Martindale abrasion tester revealed that the single-sided reduced graphene oxide–track lost its conductivity after 150 abrasion cycles, whereas the two-sided reduced graphene oxide–tracks survived 3000 abrasion cycles. These results demonstrate that a simple reduced graphene oxide coating technique can be used to prepare end-of-life sensors for high-performance textiles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it