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Record W2768622342

Mechanical Properties of Nanocrystalline Nominally Multilayered Hexagonal Cobalt Electrodeposits

2016· dissertation· en· W2768622342 on OpenAlexfundno aff
Krista Marija Viola

Bibliographic record

VenueTSpace (University of Toronto) · 2016
Typedissertation
Languageen
FieldEngineering
TopicElectrodeposition and Electroless Coatings
Canadian institutionsnot available
FundersUniversity of Toronto
KeywordsMaterials scienceMicrostructureCobaltNanocrystalline materialUltimate tensile strengthIndentation hardnessComposite materialTransmission electron microscopyMetallurgyTensile testingElongationNanotechnology
DOInot available

Abstract

fetched live from OpenAlex

The microstructure and mechanical properties of electrodeposited nanocrystalline cobalt were investigated and compared to cobalt electrodeposits produced under waveforms that would result in a nominal multilayered material by alternating electrodeposition conditions in the same electrolytic solution. All sample types were of the hexagonal crystal structure and a preferred orientation was prominent with the introduction of nominal multilayers, in which the basal plane preferentially was oriented parallel to the surface of the deposit. Transmission electron microscopy was used to compare the starting microstructure and post-failure microstructure of cobalt electrodeposits. Tensile tests were performed at a strain rate of 5 x 10-4 s-1 and microhardness tests were performed under a 100g load. The average hardness, yield, ultimate tensile and fracture strengths increased when the electrodeposited cobalt followed a nominal multilayered pulse train. Tensile elongation for cobalt electrodeposits with 100 nm nominal layer thicknesses are more than twice that observed for monolithic cobalt.

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 score0.002
Threshold uncertainty score0.005

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 head0.007
GPT teacher head0.195
Teacher spread0.188 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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Same venueTSpace (University of Toronto)Same topicElectrodeposition and Electroless CoatingsFrench-language works237,207