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Record W2063494769 · doi:10.1002/cjce.5450800315

Modeling of a process for removal of metal ions by electromigration and electrodeposition

2002· article· en· W2063494769 on OpenAlexvenueno aff
Kanchan Mondal, J. Pattanayak, Tomasz Wiltowski, Shashi B. Lalvani, N.V. Mandich

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2002
Typearticle
Languageen
FieldEngineering
TopicElectrodeposition and Electroless Coatings
Canadian institutionsnot available
Fundersnot available
KeywordsElectromigrationNickelMetalPlating (geology)Thermal diffusivityCopperImpurityPhosphoric acidChromiumMaterials scienceMass transferAnalytical Chemistry (journal)Reaction rate constantMetallurgyMetal ions in aqueous solutionIonInorganic chemistryChemistryThermodynamicsComposite materialKineticsChromatography

Abstract

fetched live from OpenAlex

Abstract A mathematical model for the removal of impurities of the metal ions of Fe, Ni, and Cu from hard chromium plating solution by electromigration and subsequent electrodeposition has been developed and presented. Experimental data for the metal removal at 45°C and constant cell voltage using o‐phosphoric acid as the catholyte are presented. Up to 36% iron and 29% nickel removal is obtained over about 25 h. The copper removal rate is observed to be approximately four times greater than the rate of nickel removal. The experimental data were found to closely match results predicted from the model developed. The inherent model parameters such as mobility, diffusivity, mass transfer coefficient and metal deposition rate constants were estimated. The calculated values of these parameters are found to be in good agreement with the published data.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.178
Teacher spread0.172 · 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 designSimulation or modeling
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

Citations1
Published2002
Admission routes1
Has abstractyes

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