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Record W2012018903 · doi:10.1115/1.2005276

Effects of Lubricants on the Friction and Wear Properties of PTFE and POM

2005· article· en· W2012018903 on OpenAlex
H. S. Benabdallah, Jianjun Wei

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Tribology · 2005
Typearticle
Languageen
FieldEngineering
TopicTribology and Wear Analysis
Canadian institutionsRoyal Military College of Canada
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceLubricationComposite materialWettingBoundary lubricationLubricityTribologyDry lubricantMetallurgy

Abstract

fetched live from OpenAlex

The friction and wear properties of PTFE and POM were investigated using a ball-on-steel ring tester under dry and lubricated conditions by paraffin and 10W–30 oils. SEM, EDAX, FTIR, and surface wettability techniques were used to characterize and assess the morphology and chemical composition of the original surfaces, as well as the wear track, transfer film, and wear debris at different loads and speeds. Although the friction was high, similar behavior to that reported in the literature was observed. The experimentally determined surface temperature of the plastic revealed optimum loading levels, for each sliding speed, at which the friction and wear rate become a minimum and the thermal effect stabilizes. In boundary-like lubrication using both oils, friction and wear were significantly reduced with the exception of an increase in wear rate with load when POM was lubricated by 10W–30 oil. Surface analysis revealed that the formation of lubricative protective layers on the surfaces in contact is crucial to reducing friction, and more importantly, wear. FTIR results confirmed that film transfer occurs in the case of POM.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.095
Threshold uncertainty score0.127

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 head0.006
GPT teacher head0.182
Teacher spread0.176 · 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