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Nickel–Alumina Functionally Graded Materials by Electrophoretic Deposition

2004· article· en· W2117646276 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

VenueJournal of the American Ceramic Society · 2004
Typearticle
Languageen
FieldEngineering
TopicElectrophoretic Deposition in Materials Science
Canadian institutionsMcMaster University
Fundersnot available
KeywordsElectrophoretic depositionNon-blocking I/ODeposition (geology)NickelSuspension (topology)Materials scienceChemical engineeringGradationSettlingMetallurgyComposite materialChemistryCoatingCatalysisEnvironmental engineering

Abstract

fetched live from OpenAlex

Electrophoretic deposition has been used to synthesize nickel–alumina, functionally graded materials from NiO and alumina suspensions in ethanol. Suspension stability and the kinetics of deposition were studied to determine optimum conditions. Deposition starts with an alumina suspension into which a stream of NiO suspension is injected at various flow rates to obtain the desired profiles. The latter were controlled by varying the deposition current density and component flow rate. The green bodies obtained were sintered in Ar/H 2 atmosphere to reduce the NiO to nickel. Various gradation profiles were obtained illustrating the facility of EPD to synthesize continuously graded materials. NiO was used as the precursor for nickel to alleviate settling and rough columnar deposit problems.

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 score0.007
Threshold uncertainty score0.548

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 head0.003
GPT teacher head0.187
Teacher spread0.184 · 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