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Record W2036540926 · doi:10.1149/2.084306jes

Ball-Milled (Cu-Ni-Fe + Fe<sub>2</sub>O<sub>3</sub>) Composite as Inert Anode for Aluminum Electrolysis

2013· article· en· W2036540926 on OpenAlexaff
S. Helle, Boyd Davis, Daniel Guay, Lionel Roué

Bibliographic record

VenueJournal of The Electrochemical Society · 2013
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsKingston Process Metallurgy (Canada)Institut National de la Recherche Scientifique
Fundersnot available
KeywordsAnodeMaterials scienceElectrolysisElectrolyteBall millMetallurgyNucleationComposite numberAluminiumInertCathodeCurrent densityChemical engineeringElectrodeComposite materialChemistry

Abstract

fetched live from OpenAlex

A (Cu-Ni-Fe + Fe2O3) composite was prepared by ball milling and evaluated as an oxygen-evolving anode for aluminum electrolysis. The material was prepared by first milling elemental Cu, Ni and Fe powders to form a Cu(Ni,Fe) solid solution. Then, the milling operation was resumed for different periods of time (from 30 min to 4 h) in presence of a fixed amount of nanosized Fe2O3 particles to achieve the desired stoichiometry (Cu65Ni20Fe15)98.6O1.4. After 4 h of milling, Fe2O3 precipitates are found to be homogeneously dispersed in the Cu-Ni-Fe matrix. The powder was then heated at 1000°C and pressed to form an electrode for evaluation in low-temperature (700°C) KF-AlF3 electrolyte at an anode current density of 0.5 A cm−2 for 20 h. The cell voltage was stable at ca. 4.5 V and the Cu, Fe and Ni contamination of the produced Al and electrolyte were quite low, resulting in an estimated anode erosion rate of 1.2 cm year−1. This good corrosion resistance is attributed to the formation of a protective NiFe2O4-rich layer on the electrode during Al electrolysis, which is likely to be favored by the presence of the finely dispersed Fe2O3 precipitates acting as nucleation sites for the formation of NiFe2O4.

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 categoriesMeta-epidemiology (narrow)
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 score0.160
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.192
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 teacher head, not a consensus.

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

Citations14
Published2013
Admission routes1
Has abstractyes

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