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Record W2029190403 · doi:10.1002/app.40350

Nylon‐coated ultra high molecular weight polyethylene fabric for enhanced penetration resistance

2014· article· en· W2029190403 on OpenAlexafffund
Dariush Firouzi, Daniel A. Foucher, Habiba Bougherara

Bibliographic record

VenueJournal of Applied Polymer Science · 2014
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceComposite materialPenetration (warfare)Nylon 6PolyethyleneCoatingImpact resistanceFiberUltra-high-molecular-weight polyethyleneNylon 66Polymer

Abstract

fetched live from OpenAlex

ABSTRACT A coating of Nylon 6,6 or 6,12 was used to improve the penetration resistance of ultra high molecular weight polyethylene (UHMWPE) fabric that would be potentially useful in the manufacture of flexible body armor against spike/knife threats. Quasi‐static test results for the spike penetrator showed a 77% and 86% improvement in the puncture resistance of Nylon 6,6 and Nylon 6,12 coated UHMWPE (respectively) over a neat fabric target of equivalent areal densities. Dynamic impact testing demonstrated dramatic improvement in the puncture resistance of nylon‐coated fabrics while only a slight improvement in stab resistance was observed comparing samples with equivalent areal densities. Photography of ruptured areas after quasi‐static testing revealed limited fiber motion or fiber stretching with no evidence of fiber pullout for nylon‐coated fabric samples in contrast to neat fabric. This suggests that there was a significant increase in energy absorption by nylon‐coated fabrics at impact. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014, 131, 40350.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.005
GPT teacher head0.213
Teacher spread0.208 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations38
Published2014
Admission routes2
Has abstractyes

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