Towards commercialization of graphene-based end-of-life sensors for fire-protective fabrics
Bibliographic record
Abstract
Abstract Fire-resistant (FR) fabrics used in protective clothing experience a reduction in performance as a result of exposure to various ageing conditions, for instance: heat, ultraviolet (UV) light, moisture, abrasion and laundering procedures. However, there are few visible clues to indicate if the deterioration of the protective clothing has reached a dangerous level. To address this issue, graphene-based end-of-life sensors (heat, UV light, and moisture) are being developed at the University of Alberta in collaboration with five industry partners, including Davey Textile Solutions, Inc (DTS). DTS has the production capacity to manufacture the graphene-based end-of-life sensors, including weaving, finishing, conductive track application, fusing, and product assembly. The lifetime of fire-protective clothing is an important parameter to monitor, and the graphene-based end-of-life sensors are a straightforward, non-destructive, and effective tool for this purpose. The plan is to fabricate, integrate and commercialize the sensors. DTS, alongside academic researchers from the University of Alberta, are in the process of scaling up the manufacturing and testing of the sensors. The health and safety of firefighters will be improved by bringing graphene-based end-of-life sensors to the market.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".