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Record W2974138660 · doi:10.1063/1.5087141

The effects of applied voltage on surface texturing during cathodic plasma electrolysis process

2019· article· en· W2974138660 on OpenAlexafffund
Wei Zha, Chen Zhao, Xueyuan Nie

Bibliographic record

VenueAIP Advances · 2019
Typearticle
Languageen
FieldEngineering
TopicTribology and Lubrication Engineering
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceTemperingTexture (cosmology)Scanning electron microscopeKurtosisSkewnessElectrolysisCathodic protectionPlasmaComposite materialSurface finishMetallurgyAnalytical Chemistry (journal)AnodeElectrodeChemistry

Abstract

fetched live from OpenAlex

Cathodic plasma electrolysis (CPE) process was applied on cast iron samples for about 1 minute to obtain a crater-liked surface texture with intention to reduce the friction and increase the wear resistance. During the treating process, the plasma discharging was initiated at the surface of cast iron samples, leading to the explosion of gas bubbles and then generating an irregular array of micro craters. The scanning electron microscopy (SEM) observations showed that the recessed and protruded surface textures were obtained when the CPE-process was conducted at low and high voltages, respectively. The textured surfaces were measured and characterized using skewness and kurtosis Pin-on-disc tribotests on those textured samples demonstrated that the samples with negative skewness and higher kurtosis had a smaller coefficient of friction (∼0.08), while the samples with positive skewness and higher kurtosis had a larger coefficient of friction (∼0.104), which was even higher than that of untreated blank sample (∼0.1). The results indicate that the applied voltage significantly influences the surface finish in terms of surface texture and morphology, and thus the coefficients of friction.

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 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.657
Threshold uncertainty score0.420

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 head0.001
GPT teacher head0.181
Teacher spread0.180 · 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 teacher head, 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

Citations2
Published2019
Admission routes2
Has abstractyes

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