Smart personal protective equipment (PPE): current PPE needs, opportunities for nanotechnology and e-textiles
Bibliographic record
Abstract
Abstract Smart personal protective equipment (PPE) is the future of improved occupational health and safety, and nanotechnology facilitates the development of critical smart PPE components such as smart textiles, wearable/flexible electronics, and augmented reality among others. Smart PPE utilizes sensing and communication technology in a way that is non-intrusive to either improve workplace safety or enhance occupational capabilities. The development of such smart PPE requires a multidisciplinary approach. This paper investigates the current state of PPE technologies for firefighters, healthcare workers, police/military, and construction workers. The modern PPE needs are identified from both end user surveys as well as expert third-party studies. There are already some smart PPE solutions for the challenges identified. Recent advances in stretchable and textile-based electronics, enabled by nanotechnology, demonstrate almost all imaginable solutions to the unmet needs that PPE users and expert advisor groups have identified. However, integration into smart PPE requires attention to the unique harsh conditions of hazardous workplaces. This review aims to inspire researchers in the field of flexible and printed electronics to develop and improve future smart PPE.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".