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Record W2073085106 · doi:10.1021/cm035306u

Electroplating of Nanocrystalline CoFeNi Soft Magnetic Thin Films from a Stable Citrate-Based Bath

2004· article· en· W2073085106 on OpenAlexafffund
Yahui Zhang, Douglas G. Ivey

Bibliographic record

VenueChemistry of Materials · 2004
Typearticle
Languageen
FieldEngineering
TopicElectrodeposition and Electroless Coatings
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectroplatingMaterials scienceNanocrystalline materialThin filmPlating (geology)FabricationCurrent densityGrain sizeMetallurgyChemical engineeringNanotechnologyLayer (electronics)

Abstract

fetched live from OpenAlex

CoFeNi alloys are some of the most studied soft magnetic materials because of their superior properties over FeNi alloys as write head core materials in hard-disk-drives. Electroplating processes have major significance in the fabrication of thin-film recording heads due to their advantages of simplicity, cost-effectiveness, and controllable patterning. Conventional low pH (2.5−3.0) baths suffer from problems such as poor stability, low current density efficiency, and voids in deposited films due to the electroplating of hydrogen. A new, stable citrate-based bath has been developed in this study. Citrate can effectively improve the stability of CoFeNi plating baths, and denser CoFeNi films can be plated out because of the higher bath pH (>5). The effects of bath composition and plating conditions on the electroplating of CoFeNi thin films have been studied. CoFeNi thin films with preferred composition, mixed fcc-bcc phases, and 10−20 nm grain sizes have been plated out from the new citrate-based bath.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.986

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 head0.004
GPT teacher head0.173
Teacher spread0.169 · 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

Citations34
Published2004
Admission routes2
Has abstractyes

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