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Record W3017677168 · doi:10.1016/j.jobab.2020.04.007

Analysis and comparison of tribological performance of fatty acid-based lubricant additives with phosphorus and sulfur

2020· article· en· W3017677168 on OpenAlexvenueno aff
Haiyang Ding, Xiaohua Yang, Lina Xu, Mei Li, Shouhai Li, Sujing Zhang, Jianling Xia

Bibliographic record

VenueJournal of Bioresources and Bioproducts · 2020
Typearticle
Languageen
FieldEngineering
TopicLubricants and Their Additives
Canadian institutionsnot available
FundersNational Key Research and Development Program of China
KeywordsLubricantRicinoleic acidTribologySulfurMaterials scienceX-ray photoelectron spectroscopyScanning electron microscopeLubricationChemical engineeringPhosphorusAcid valueCastor oilNuclear chemistryChemistryMetallurgyComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Two environmentally friendly, water-based lubricant additives (phosphorus-containing ricinoleic acid (PRA) and sulfur-containing ricinoleic acid (SRA)) were prepared. The lubrication performance of the additives in a water-based lubricant was tested using a four-ball tribotester. The stainless steel surface was analyzed by using scanning electron microscopy (SEM) and X-ray photoelectron spectroscopy (XPS). The additives reduced the coefficient of friction (COF) value, wear scar diameter (WSD) and improved the extreme pressure (PB) value. Water-based fluids containing the PRA exhibited lower COF, WSD, and PB values than the SRA. The good tribological performances of the PRA and SRA were attributed to the synergistic action of long aliphatic chains and highly active phosphorus and sulfur elements.

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)0.001
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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.207
Teacher spread0.194 · 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

Citations33
Published2020
Admission routes1
Has abstractyes

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