MétaCan
Menu
Back to cohort
Record W2112648249 · doi:10.1149/1.2948362

Saccharin Effects on Direct-Current Electroplating Nanocrystalline Ni–Cu Alloys

2008· article· en· W2112648249 on OpenAlexafffund
Xinwei Cui, Weixing Chen

Bibliographic record

VenueJournal of The Electrochemical Society · 2008
Typearticle
Languageen
FieldEngineering
TopicElectrodeposition and Electroless Coatings
Canadian institutionsUniversity of Alberta
FundersUniversity of Alberta
KeywordsMaterials scienceNanocrystalline materialDendrite (mathematics)MetallurgyAlloyGrain sizeElectroplatingPlating (geology)CoatingSaccharinCopperChemical engineeringComposite materialNanotechnology

Abstract

fetched live from OpenAlex

Poor surface finish and coarse dendrite structure are the major challenges in direct-current (dc) plating of nanosized Ni–Cu alloy coatings. This investigation was initiated to understand the effect of saccharin on the formation of nanosized Ni–Cu alloy coatings by sediment codeposition, and the role of saccharin in improving surface finish and suppressing coarse dendrite growth during sediment codeposition. It was found that only 0.5 g/L addition of saccharin could form dendrite-free nanocrystalline Ni–Cu alloy coatings with a mirror-finish surface. The Cu content in the Ni–Cu alloy coatings can be controlled to be as low as 10 wt % by changing the current density. The grain size in the coatings was determined by X-ray diffraction and electron microscopy analysis to be 15.7 nm on average. The amount of ordered -type Ni–Cu nanophase was found to be insignificant in comparison with that in pulse plated coatings. Saccharin suppresses the reduction of Cu and acts as a leveling and grain size reduction agent in Ni–Cu alloy codeposition. From steady-state polarization and impedance analysis, it is believed that these saccharin effects are produced by the formation of Ni–Saccharin complexes adsorbed on the coating surface that suppress Ni–Cu dendrite growth.

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 score0.009
Threshold uncertainty score0.896

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.006
GPT teacher head0.202
Teacher spread0.196 · 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

Citations26
Published2008
Admission routes2
Has abstractyes

Explore more

Same venueJournal of The Electrochemical SocietySame topicElectrodeposition and Electroless CoatingsFrench-language works237,207