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Microstructural and Mechanical Characterization of Multilayered Iron Electrodeposits

2011· article· en· W2090600174 on OpenAlex
C. W. Chan, J.L. McCrea, G. Palumbo, U. Erb

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAdvanced materials research · 2011
Typearticle
Languageen
FieldMaterials Science
TopicMicrostructure and mechanical properties
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Tokyo
KeywordsMaterials scienceMicrostructureIndentation hardnessPlating (geology)MetallurgyGrain sizeVickers hardness testCharacterization (materials science)Scanning electron microscopeComposite materialNanotechnology

Abstract

fetched live from OpenAlex

Monolithic and multilayered iron electrodeposits were successfully synthesized by the pulse plating electrodeposition method. Electron microscopy and Vickers microhardness measurements were used to investigate the microstructure and mechanical properties of the iron electrodeposits produced. Two types of monolithic iron coatings were produced, one with a coarse grained, columnar structure and the other with an ultra-fine grained structure. Hall-Petch type grain size strengthening was observed in these monolithic coatings. Multilayered iron coatings composed of alternating layers of coarse grained and fine grained structures were also produced. The hardness value of the multilayered coatings falls between the hardness values for the two types of monolithic coatings produced. This study has demonstrated the possibility of applying a multilayered structure design to tailor the microstructure and mechanical properties of electrodeposited iron coatings.

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)0.001
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 score0.004
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.049
GPT teacher head0.308
Teacher spread0.259 · 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