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Record W2330945186 · doi:10.1149/1.3140017

Electrodeposition of an Iron-Cobalt Phase Isostructural to α-Mn

2009· article· en· W2330945186 on OpenAlexafffund
B. Crozier, Qi Liu, Douglas G. Ivey

Bibliographic record

VenueECS Transactions · 2009
Typearticle
Languageen
FieldEngineering
TopicElectrodeposition and Electroless Coatings
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Alberta
KeywordsMaterials scienceAnnealing (glass)IsostructuralPhase (matter)CobaltMetallurgyMicrostructureMetastabilityCrystallographyChemistryCrystal structure

Abstract

fetched live from OpenAlex

Electrodeposition at elevated temperatures from a dibasic ammonium citrate-stabilized iron-cobalt electroplating solution is shown to promote the deposition of a metastable phase. The metastable phase is isostructural to α-Mn. XRD analysis indicated that the α-Mn-type phase begins to be deposited at temperatures between 30{degree sign}C and 40{degree sign}C. Below this the equilibrium BCC phase is deposited. Plating at 60{degree sign}C produced deposits composed completely of the α-Mn-type phase. TEM analysis indicated that increasing the plating temperature refined the deposit's grain structure and also led to regions with preferred orientation. Annealing of deposits composed of the α-Mn-type phase above its transformation temperature resulted in the formation of a textured BCC phase. The presence of the α-Mn-type phase led to high deposit coercivities. Annealing of deposits, which resulted in transformation of the α-Mn-type phase to the BCC phase, reduced their coercivities. The coercivities of these films, however, were not reduced to the level of those deposits which were plated as the BCC phase. In deposits completely composed of the BCC phase, either plated as such or transformed, annealing led to higher coercivities. This is attributed to coarsening of the deposit microstructure.

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 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 score0.235
Threshold uncertainty score0.648

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 head0.004
GPT teacher head0.233
Teacher spread0.229 · 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 teacher head, 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

Citations5
Published2009
Admission routes2
Has abstractyes

Explore more

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