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Record W4382312339 · doi:10.1016/j.rsurfi.2023.100117

Binary and ternary lubricious oxides for high temperature tribological applications: A review

2023· review· en· W4382312339 on OpenAlexafffund
Amit Roy, Payank Patel, Navid Sharifi, Richard R. Chromik, Pantcho Stoyanov, Christian Moreau

Bibliographic record

VenueResults in Surfaces and Interfaces · 2023
Typereview
Languageen
FieldEngineering
TopicMetal and Thin Film Mechanics
Canadian institutionsConcordia UniversityMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaConsortium de Recherche et d’innovation en Aérospatiale au Québec
KeywordsTribologyMaterials scienceTernary operationOxideFriction coefficientDry lubricantMetallurgyCoefficient of frictionComposite materialComputer science

Abstract

fetched live from OpenAlex

Oxides and oxide-based coatings have been widely used as solid lubricants in demanding operating conditions to achieve low friction and wear due to their higher thermal and chemical stability. However, their tribological performance is highly dependent on the test temperatures and the surrounding environment. This article provides a comprehensive review of low-friction oxides and oxide-based coatings in relation to the influence of operating temperature on their tribological performance and their potential use as solid lubricants. Special emphasis is placed on the tribological behavior of binary and ternary oxides developed over the last few decades. Furthermore, this review summarizes the high temperature tribology, mechanisms and interfacial processes of the oxides leading to low friction coefficient and wear in high temperature applications.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.051
GPT teacher head0.313
Teacher spread0.262 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations25
Published2023
Admission routes2
Has abstractyes

Explore more

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