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Record W4285737350 · doi:10.18280/rcma.320306

Tribological and Mechanical Performance of Epoxy Reinforced by Fish Scales Powder

2022· article· en· W4285737350 on OpenAlex

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueRevue des composites et des matériaux avancés · 2022
Typearticle
Languageen
FieldEngineering
TopicTribology and Wear Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsEpoxyMaterials scienceComposite materialTribologyUltimate tensile strengthMatrix (chemical analysis)

Abstract

fetched live from OpenAlex

The study was conducted to use fish scales powder as animal biomass to prepare epoxy composites. Fish scales powder is beneficial in reducing environmental pollutants. The fish scales powder was added to the epoxy matrix for improving the interfacial bonding between the scales and the epoxy matrix. However, a direct method was used to prepare epoxy composites, and samples were cut according to ASTM standards for mechanical and tribological tests. Interfacial interaction between the fish scales powder and epoxy was investigated by FTIR and SEM. It was found that the fish scales powder contents affect the mechanical properties and tribological behaviour of produced composites. Compared to pure epoxy, the load of 10 wt.% fish scales powder increased the tensile strength by 16.0%. As well as, the coefficient of friction was reduced by 16.0% and wear resistance was enhanced by 48.58%. The improvements in the performance of composites are contributed to the hydrogen bonding formed between fish scales powder and epoxy matrix.

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 categoriesnone
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 score0.455
Threshold uncertainty score0.652

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 integrity0.0000.000
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 head0.021
GPT teacher head0.226
Teacher spread0.205 · 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