Life-cycle risk assessment of graphene-enabled textiles in fire protection gear
Bibliographic record
Abstract
A nanomaterial life-cycle risk assessment (Nano LCRA) of a graphene-enabled textile used in the construction of heat and fire-resistant personal protective equipment (PPE) was conducted to develop, analyze, and prioritize potential occupational, health and environmental risks. The analysis identifies potential receptors and exposure pathways at each product life-cycle stage and makes a qualitative evaluation of the potential significance of each scenario. A literature review, quality evaluation, and database were developed as part of the LCRA to identify potential hazards associated with graphene-based materials (GBMs) throughout the product life-cycle. Generally, risks identified from graphene-enabled textiles were low. Of the developed exposure scenarios, occupational inhalation exposures during raw material and product manufacturing ranked highest. The analysis identifies the key potential human and environmental hazards and exposures of the products across the product life-cycle of graphene enabled textiles. Priority research gaps to reduce uncertainty include evaluating long-term, low dose graphene exposures typical of the workplace, as well as the potential release and hazard characterization of graphene-acrylic nanocomposites.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".