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Record W4392164852 · doi:10.1177/14658011241234194

Scratch performance of natural rubber and natural rubber composites reinforced with nylon, kevlar, and carbon fabrics

2024· article· en· W4392164852 on OpenAlex
Xin Wang, Shing‐Chung Wong, Xiaosheng Gao, Soon Won Moon, Yongsong Xie

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePlastics Rubber and Composites Macromolecular Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicTribology and Wear Analysis
Canadian institutionsNational Research Council CanadaSyncrude (Canada)
Fundersnot available
KeywordsComposite materialMaterials scienceScratchKevlarNatural rubberTearingPenetration (warfare)StiffnessIndentationEpoxy

Abstract

fetched live from OpenAlex

The scratch performance of natural rubber (NR) and fibre-reinforced NR was investigated using a scratch test with acoustic emission (AE). Both maximum penetration depth and maximum tangential force were characterised by two procedures. Procedure 1 applied the corner of a steel cube at face leading orientation. Procedure 2 applied a steel pyramid indenter with a spherical tip at the edge leading orientation. AE was adopted in Procedure 2 for evaluation of cutting damage mode that varied from ploughing/tearing the matrix to cutting both the matrix and fabric. The scratched regions of all specimens were observed using an optical microscope to estimate the damage level and examine damage mechanisms. The results show that scratch resistance improved as fibre content and fibre stiffness increased. The maximum tangential force depended on the damage modes. Two or more fabric layers could further increase penetration resistance, but the damage level is more serious in most cases.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.708
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.001
GPT teacher head0.155
Teacher spread0.154 · 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