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Record W3025791301 · doi:10.1149/ma2020-01522911mtgabs

(Co,Ni)O Coated Anodes for CO<sub>2</sub>-Free Al Production

2020· article· en· W3025791301 on OpenAlexaffabout
Saeed Mohammadkhani, Vahid Jalilvand, Ali Dolatabadi, Christian Moreau, Boyd Davis, Daniel Guay, Lionel Roué

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldChemical Engineering
TopicMolten salt chemistry and electrochemical processes
Canadian institutionsKingston Process Metallurgy (Canada)Concordia UniversityInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsAnodeElectrolysisMaterials scienceInertInert gasNickelMetallurgyFerrite (magnet)ElectrochemistryAluminiumChemical engineeringElectrodeChemistryComposite material

Abstract

fetched live from OpenAlex

Canadian aluminum production is an important source of greenhouse gases (GHGs) with 6 Mt of CO 2 eq emitted in 2017, which is equivalent to the amount of GHG generated annually by about 2 million cars. The current technology consumes carbon anodes during the electrolysis of aluminum to form CO 2 according to the overall reaction: Al 2 O 3 + 3/2 C = 2 Al + 3/2 CO 2 . The most effective solution would be to replace the consumable carbon anodes with so-called inert anodes that emit O 2 rather than CO 2 and that are based on the following overall reaction: Al 2 O 3 = 2 Al + 3/2 O 2 . This would reduce GHGs by 75 to 100% depending on the type of emissions (CO 2 , CF x , NO x , SO x , etc.). However, the design of inert anodes is a major challenge because of the severe conditions during aluminum electrolysis that require materials with excellent corrosion and thermal shock resistance while having the same adequate electrochemical properties [1]. Among inert anodes studied so far, Cu-Ni-Fe-based alloys appear to be the most promising owing to their ability to form a layer of nickel ferrite (NiFe 2 O 4 ) on the surface of the anode upon Al electrolysis [2-4]. This nickel ferrite has low solubility in a cryolithic medium. However, the formation of the nickel ferrite protective layer is slow and, depending on the experimental conditions, might not be formed fast enough to provide effective protection of the underlying substrate Cu-Ni-Fe alloy. One strategy to help in the formation of this layer is to coat the Cu-Ni-Fe alloy with a sacrificial layer that would be stable for a period long enough to allow the formation of NiFe 2 O 4 on the surface of the anode. In this context, the use of (Co,Ni)O-based protective coatings for metallic anode appears promising [5]. However, it is challenging to produce 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 thermal spray techniques such as suspension plasma spray (SPS) and high velocity oxygen fuel (HVOF). They are well-established technologies for producing protective oxide coatings for various industrial applications ( e.g. gas turbines). Additionally, thermal spray could be used on Al production site to restore protective (Co,Ni)O coating on end-of-life inert anodes. As a first step toward this goal, single phase (Co,Ni)O powders with various Co/Ni ratios that could be used as raw materials for the thermal spraying of protective coatings onto Cu-Ni-Fe inert anodes have been produced [6]. The crystalline structure, thermal stability, electrical conductivity and solubility in cryolite media of the produced (Co,Ni)O powders are characterized depending on their Co/Ni ratio. Finally, selected (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 and the electrochemical behaviour of the coated inert anodes under Al electrolysis conditions is presented. 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

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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)0.004
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 score0.024
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
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 head0.019
GPT teacher head0.246
Teacher spread0.227 · 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

Citations0
Published2020
Admission routes2
Has abstractyes

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