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Record W4248137886 · doi:10.1149/ma2020-02503759mtgabs

Synthesis and Characterization of (Co,Ni)O Solid Solutions As Protective Coatings for Inert Anodes in Aluminum Electrolysis

2020· article· en· W4248137886 on OpenAlexaboutno aff
Saeed Mohammadkhani, Vahid Jalilvand, Ali Dolatabadi, Christian Moreau, Boyd H. Davis, Daniel Guay, Lionel Roué

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceAnodeInertElectrolysisElectrolyteMetallurgyDissolutionCorrosionCryoliteElectrochemistryAluminiumElectrolytic processCarbon fibersChemical engineeringChemistryElectrodeComposite material

Abstract

fetched live from OpenAlex

The industrial production of primary aluminum from alumina ore (Al 2 O 3 ) is still carried out by the Hall-Héroult process. The process relies on dissolving alumina in an electrolyte consisting mainly of liquid Na 3 AlF 6 at 950-1000 °C. While the reduction of dissolved Al 3+ ions in aluminum occurs at the cathode, the complementary reaction occurs at a carbon anode that is consumed to form carbon dioxide, which necessitates its regular replacement (every 25 days). The overall reaction is the following: Canadian aluminum smelters produce annually about 6 Mt of CO 2 eq (reported in 2017), which is equivalent to the CO 2 amount generated annually by about 2 million cars. The substitution of consumable carbon anodes with inert (O 2 -evolving) anodes in the electrochemical cells to produce aluminium would have significant environmental benefits because it would eliminate the emissions of carbon dioxide and perfluorocarbons associated with the consumption of the carbon anode. However, the design of inert anodes is a major challenge because of the severe Al electrolysis conditions (cryolithic medium at 960 °C), which requires materials with excellent resistance to corrosion and thermal shock as well as adequate electrochemical properties [1]. Single phase Cu 65 Ni 20 Fe 15 alloy is a promising inert anode for Al production due to its ability to form a protective NiFe 2 O 4 layer upon Al electrolysis. However, its corrosion resistance is still insufficient and a protective layer is required to prevent the penetration of electrolyte into the nickel ferrite layer and the formation of fluorides during Al electrolysis [2-4]. In this context, the use of (Co,Ni)O-based protective coatings for metallic anode appears promising [5]. However, it is challenging to prepare a single phase, coherent and crack-free oxide layer as required for industrial Al production. A potentially relevant method to produce (Co,Ni)O coated inert anodes is by direct deposition of (Co,Ni)O oxide compounds by spray deposition techniques such as suspension plasma spray (SPS) and high velocity oxygen fuel (HVOF). These additive manufacturing deposition are well-established technologies for producing protective oxide coatings for various industrial applications ( e.g. gas turbines). As a first step toward this goal, pure Co x Ni 1−x O solid solutions have been prepared over the whole composition range by a two-step procedure that consisted of high-energy ball milling followed by a heat treatment of Co 3 O 4 and NiO powders [6]. Then, the thermal stability and electrical conductivity of (Co,Ni)O solid solutions were determined at temperatures ranging between 700 and 1000 °C. Also, their dissolution rate was measured in K-based cryolite at 700 °C and in Na-based cryolite at 1000 °C. In a second step, (Co,Ni)O powders have been used as raw materials for the thermal spraying (HVOF and SPS) of protective coatings onto Cu-Ni-Fe inert anodes [7]. The morphological and microstructural characteristics of the coating depending on the thermal spray conditions will be presented. The oxidation behaviour of the coated inert anodes under air and argon is presented. Preliminary investigation of the electrochemical behaviour under Al electrolysis conditions of the (Co,Ni)O/Cu-Ni-Fe anodes will be also presented and discussed. References [1] I. Galasiu et al., Aluminium-Verlag, Düsseldorf (2007). [2]S. Jucken et al., Corr. Sci. (2019) 147: 321-329. [3]E. Gavrilova et al., Corr. Sci. (2015) 101: 105-113. [4] S. Helle et al., Corr. Sci. (2010) 52:3348-3353. [5] T. Nguyen et al., Light Metals (2006) 385-390. [6] S. Mohammadkhani et al., J. Am. Ceram. Soc. (2019) 102: 5063– 5070. [7] S. Mohammadkhani et al., Surf. Coat. Tech. (2020) 399: 126168.

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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)0.001
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 score0.019
Threshold uncertainty score0.526

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.020
GPT teacher head0.251
Teacher spread0.231 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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