MétaCan
Menu
Back to cohort
Record W2069745686 · doi:10.2320/matertrans.46.2169

Effect of Zincate Treatment on Adhesion of Electroless Ni–P Plated Film for 2017 Aluminum Alloy

2005· article· en· W2069745686 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMATERIALS TRANSACTIONS · 2005
Typearticle
Languageen
FieldEngineering
TopicElectrodeposition and Electroless Coatings
Canadian institutionsImperial Metals (Canada)
Fundersnot available
KeywordsZincateAdhesionMaterials scienceSubstrate (aquarium)AluminiumZincAlloyPlating (geology)MetallurgyComposite material

Abstract

fetched live from OpenAlex

The present authors have investigated the changes in adhesion of electroless Ni–P plated films on an aluminum alloy substrate (JIS A2017P-T3, Al–4 mass%Cu) from the viewpoint of the preceding zincate treatment and the subsequent heat treatment. The precipitation state of zinc and adhesion of Ni–P plated film differed with the number of zincate treatments. Without zincate treatment, some of the Ni–P plated film peeled off during the plating process, and the film obtained after the single zincate treatment also showed poor adhesion to the substrate. The highest adhesion was obtained by the double zincate treatment, and the triple zincate treatment resulted in a poorer adhesion. Existence of aluminum at the surface of the zincate film was shown to be necessary to obtain higher adhesion of the Ni–P plated film, on the other hand, excess zinc obtained by the triple zincate caused lower adhesion. Interdiffusion of aluminum and nickel between the Ni–P plated film and the substrate through the medium of zincate film with an appropriate thickness is thought to promote strong bond between the Ni–P plated film and the substrate.

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.

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 score0.015
Threshold uncertainty score0.854

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)0.0000.000

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 head0.009
GPT teacher head0.244
Teacher spread0.235 · 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