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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 OpenAlexvenueno aff
Ali Kifah Ghazi, Ayad Abid Muhmmed, Nabil Kadhim Taieh, Mohanad Kadhim Mejbel

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.

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 head0.021
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

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

Citations19
Published2022
Admission routes1
Has abstractyes

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Same venueRevue des composites et des matériaux avancésSame topicTribology and Wear AnalysisFrench-language works237,207