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High temperature performance of Mn Co oxide coatings deposited by HiPIMS for interconnect application in Solid Oxide Cell

2025· article· en· W4408224722 on OpenAlexaff
Théo Dejob, Mathilde Bouvier, Antoine Casadebaigt, Jaâfar Ghanbaja, Fabien Rouillard, Frédéric Sanchette

Bibliographic record

VenueSurface and Coatings Technology · 2025
Typearticle
Languageen
FieldMaterials Science
TopicZnO doping and properties
Canadian institutionsCentre for Excellence in Mining Innovation
Fundersnot available
KeywordsMaterials scienceOxideInterconnectionHigh-power impulse magnetron sputteringChemical engineeringOptoelectronicsNanotechnologyMetallurgyThin filmSputteringComputer scienceSputter depositionEngineeringTelecommunications

Abstract

fetched live from OpenAlex

To decrease the volatilization rate of Cr on AISI 441 interconnect for Solid Oxide Cells (SOC) application, Mn 0.5 Co 0.5 O coatings were deposited using Reactive High Power Impulse Magnetron Sputtering (R-HiPIMS). Three different coating morphologies and preferential growth orientations were obtained by varying bias and temperature settings. All synthetized Mn 0.5 Co 0.5 O coatings reduced the volatilization rate of Cr during exposure in dry air at 800 °C for 2000 h. Moreover, the Cr retention power was influenced by the preferential growth orientation of the coating. This study suggests that the deposition parameters of R-HiPIMS can be adjusted to optimize the crystalline orientation and enhance the effectiveness of Mn Co oxide coatings as barriers against chromium volatilization. • Coatings of (Mn,Co)O and (Mn,Co) 3 O 4 oxides were synthetized by R-HiPIMS on AISI 441 stainless steel. • Different (Mn,Co)O structures were made by adjusting polarization and deposition temperature. • The Cr retention capacity of the coating at 800 °C was influenced by the coating structure. • (Mn,Co)O coating showed good results against Cr volatilization for a SOC application.

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 score0.009
Threshold uncertainty score0.549

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 head0.005
GPT teacher head0.230
Teacher spread0.226 · 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

Citations4
Published2025
Admission routes1
Has abstractyes

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