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Record W2346484584 · doi:10.14447/jnmes.v14i4.96

A Study on the Hydrogen Evolving Activity of Electroplated Ni-P Coating by Using the Taguchi Method

2011· article· en· W2346484584 on OpenAlexvenueno aff
Hung‐Bin Lee, Chen-Hsiung Hsu, Dong‐Sing Wuu

Bibliographic record

VenueJournal of New Materials for Electrochemical Systems · 2011
Typearticle
Languageen
FieldEngineering
TopicElectrodeposition and Electroless Coatings
Canadian institutionsnot available
Fundersnot available
KeywordsCoatingDuty cycleMaterials scienceGrain sizeElectroplatingElectrolyteInternal stressSurface finishSurface roughnessStress (linguistics)Taguchi methodsMetallurgyHeavy dutyComposite materialLayer (electronics)ChemistryVoltageElectrode

Abstract

fetched live from OpenAlex

The optimal catalytic activity of the electrodeposited Ni-P coating with the control of the process parameters was performed in this study by using Taguchi’s method. The controlled process parameters included current density, duty cycle and the concentration of the phosphorous acid in the electrolyte. The correlation among the controlled parameters and the resulted hydrogen evolution reaction (HER) activity was discussed with emphasis on the influence of the P content, internal stress, roughness and grain size of the coating. The optimal HER property was obtained for the electrodeposition of Ni-P coating with a phosphorous acid concentration of 5g/l, current density of 16A/dm2 and duty cycle of 100 %. The resulted Ni-P coating with a better HER property was the one with P content 3at%, an internal stress higher than 10MPa, grain size 10~15nm and surface roughness higher than 100nm. In addition, the cracking of the coating due to high internal stress favored the raise of the HER activity.

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)0.002
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 score0.024
Threshold uncertainty score0.670

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.032
GPT teacher head0.270
Teacher spread0.237 · 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

Citations3
Published2011
Admission routes1
Has abstractyes

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