MétaCan
Menu
Back to cohort

Development of Wear Resistant Nano Dispersed Composite Coating by Electrodeposition

2010· article· en· W2032643367 on OpenAlexaff
Ratan K. Saha, Ikram ul Haq, Tahir I. Khan, L. B. Glenesk

Bibliographic record

VenueKey engineering materials · 2010
Typearticle
Languageen
FieldEngineering
TopicElectrodeposition and Electroless Coatings
Canadian institutionsUniversity of CalgaryHyperion Technologies (Canada)
FundersHigher Education Commision, PakistanHigher Education Commission, Pakistan
KeywordsMaterials scienceCoatingBoric acidDispersion (optics)Composite numberScratchNickelMetallurgyVickers hardness testElectrolyteComposite materialNano-Indentation hardnessMicrostructureElectrodeChemistry

Abstract

fetched live from OpenAlex

This study concerns the development of wear resistant coatings of Ni-Al2O3 composite on steel substrates by electrodeposition. Each of the coating experiments was performed in an electrolytic bath, containing a nano-sized dispersion of Al2O3 particles in nickel sulfate and boric acid solution. Composition of the coating mixture was systematically varied with respect to the contents of the dispersed particles, while the amount of the dissolved nickel sulfate, and boric acid and the applied current were kept constant during the experimental measurements. The coated substrates were characterized for their morphology, Vickers hardness, and scratch resistance properties. It was observed that hardness and scratch resistance of the coated substrates increased with an increase in the Al2O3 content in the coating. It was noted that hardness of the composite coating decreased after heat treatment at 400oC in air atmosphere.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 head0.003
GPT teacher head0.171
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 source (direct Gemma or distilled Codex), 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

Citations11
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueKey engineering materialsSame topicElectrodeposition and Electroless CoatingsFrench-language works237,207