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Effect of electrolyte additives on microstructure and properties of electroplated chromium coatings

2011· article· en· W2019787419 on OpenAlexafffund
Igor Makarov, J Chen, E Yurasovskaya, John McCracken, H. Henein

Bibliographic record

VenueCanadian Metallurgical Quarterly · 2011
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsUniversity of British ColumbiaUniversity of AlbertaAlberta Environment and Protected Areas
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectroplatingCathodeMicrostructureChromiumElectrolyteMaterials sciencePlating (geology)Chrome platingMetallurgyHexavalent chromiumCathodic protectionCurrent densityChemical engineeringComposite materialAnodeElectrodeChemistryLayer (electronics)

Abstract

fetched live from OpenAlex

Recent progress in environmental science provides an incentive to explore the use of hexavalent chromium for anticorrosion and wear protection purposes. To this end, this work aims to explore the evolution of the microstructure of chromium coatings as a function of electric current through the electrolyte in the presence of organic and non-organic additives. In this study, the fundamental properties of electroplating related to hard chromium such as hardness (wear resistance) and cathode efficiency are reviewed with respect to cathode current. The results obtained show that the microstructure is sensitive to the presence of organic admixtures in the plating solution. In addition, the effect of hard microparticles on the plating process at enhanced values of cathode current is reported. The cathode efficiency for different plating speeds and in different chromium electrolytes is calculated from the experimental results.

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 categoriesInsufficient payload (model declined to judge)
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 score0.005
Threshold uncertainty score1.000

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)0.0010.000

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 head0.010
GPT teacher head0.201
Teacher spread0.191 · 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.

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

Citations4
Published2011
Admission routes2
Has abstractyes

Explore more

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