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Recent Developments and Needs in Materials Used for Personal Protective Equipment and Their Testing

2009· article· en· W2014570894 on OpenAlexaff
Patricia I. Dolez, Toan Vu‐Khanh

Bibliographic record

VenueInternational Journal of Occupational Safety and Ergonomics · 2009
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsPersonal protective equipmentRisk analysis (engineering)EngineeringClothingEmerging technologiesField (mathematics)Forensic engineeringMaterials testingConstruction engineeringArchitectural engineeringManufacturing engineeringComputer scienceCoronavirus disease 2019 (COVID-19)BusinessMedicine

Abstract

fetched live from OpenAlex

The field of personal protective equipment (PPE) has led to several high technology innovations. Indeed, improved protection against the various possible encountered risks is looked for, in particular at the workplace. This has generated the development of new materials and new manufacturing technologies, as well as the introduction of new applications for existing ones. However, the remaining challenges are numerous. This paper presents some of the new technologies introduced in the field of protective clothing against heat and flames, mechanical risks and chemical aggressors. It also describes new challenges that are currently worked on, in particular the effect of service aging and the need for testing methods that reproduce realuse conditions. Finally, it discusses various existing and potential applications of nanomaterials and smart textiles for 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 imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.004
Open science0.0020.001
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.042
GPT teacher head0.286
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations53
Published2009
Admission routes1
Has abstractyes

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