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Record W3209467916 · doi:10.1021/acsapm.1c01301

Influence of Topical Cross-Linking on Mechanical and Ballistic Performance of a Woven Ultra-High-Molecular-Weight Polyethylene Fabric Used in Soft Body Armor

2021· article· en· W3209467916 on OpenAlexafffund
Mathieu L. Lepage, Mahdi Takaffoli, Chakravarthi Simhadri, Ryan Mandau, Mazeyar Parvinzadeh Gashti, Rashid Nazir, Majid Mohseni, Wu Li, Chang Liu, Liting Bi, Greg Falck, Peter Berrang, Kevin Golovin, Abbas S. Milani, Gino A. DiLabio, Jeremy E. Wulff

Bibliographic record

VenueACS Applied Polymer Materials · 2021
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsUniversity of TorontoUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaUniversity of Victoria
FundersMinistère de la Défense NationaleMitacsCanada Research Chairs
KeywordsMaterials sciencePolyethyleneComposite materialBallistic impactWoven fabricArmourUltra-high-molecular-weight polyethyleneBallistic limitPerforationComposite numberProjectile

Abstract

fetched live from OpenAlex

We demonstrate that topical cross-linking of woven ultra-high molecular weight polyethylene (UHMWPE) fabric with a small-molecule bis-diazirine reagent can enhance the fabric’s resistance to tear and perforation and could provide a lead to further increase the protection provided by a soft body armor made from UHMWPE materials. A series of experiments confirms that the enhancement is likely due to cross-links formed within and between the yarns constituting the woven fabric and that cross-linking particularly strengthens the fabric along its bias direction. The treated fabric was incorporated into multilayer composite panels and challenged in ballistic tests. These measurements revealed that as low as a 1 wt % cross-linker can significantly increase the V 50 ballistic limit by 18 m/s.

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 score0.001
Threshold uncertainty score0.003

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)0.0010.000

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 head0.007
GPT teacher head0.227
Teacher spread0.220 · 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

Citations17
Published2021
Admission routes2
Has abstractyes

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Same venueACS Applied Polymer MaterialsSame topicMechanical Behavior of CompositesFrench-language works237,207