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Record W3185219312 · doi:10.1149/ma2021-0124925mtgabs

Electrophoretic Deposition of Aluminum Particles and Control of the Coating Microstructure

2021· article· en· W3185219312 on OpenAlexaff
Julien Wagner, Florence Ansart, Pierre‐Louis Taberna, Léa Gani, Stéphane Knittel

Bibliographic record

VenueECS Meeting Abstracts · 2021
Typearticle
Languageen
FieldEngineering
TopicElectrophoretic Deposition in Materials Science
Canadian institutionsSafran Electronics (Canada)
Fundersnot available
KeywordsElectrophoretic depositionMicrostructureCoatingMaterials scienceElectrophoresisDeposition (geology)Chemical engineeringSuspension (topology)CeramicAluminiumComposite materialMetallurgyChemistryChromatography

Abstract

fetched live from OpenAlex

Electrophoretic deposition (EPD) is an efficient process for the elaboration of homogeneous thickness and tunable microstructure coating on conductive substrates [1]. EPD displays several advantages such as a low operating cost, a relatively simple equipment and the ability to plate uniformly onto complex shape parts [1]. Among all studies on electrophoretic deposition, metallic particles are widely less investigated than ceramic ones. However metallic materials offer interesting properties (optics, catalysis, etc.). The use of alcoholic media is widely advantageous for electrophoretic deposition process because a significant voltage range can be investigated while avoiding water electrolysis and bubbles formation. Moreover, as some metals present high reactivity towards aqueous media, the choice of alcohols as dispersing media looks appropriated. Electrophoretic deposition of an aluminum powder was performed in different alcoholic media: ethanol and propan-2-ol. The nature of the dispersing medium appears to strongly influence the process deposition. Direct EPD from pure ethanol suspension is not convenient [2] whereas deposition from propan-2-ol medium leads to an homogenous coating (Figure 1). Nevertheless, addition of ionic species into the ethanol suspension allows an increase of the number of aluminum particles deposited. The addition of these species also lead the formation of homogenous thickness coating up to 100 µm. The influence of the key parameters (relative to the suspension and the electrophoretic process) has been investigated in relationship to the coatings thickness and the microstructure. Keywords: electrophoretic deposition, aluminum, coating, microstructure. Figure 1: FIB-prepared cross-section of Al coating deposited under 10 V.cm-1 during 10 min from propan-2-ol suspension. References: 1. L. Besra and M. Liu, “A review on fundamentals and applications of electrophoretic deposition (EPD),” Prog. Mater. Sci., vol. 52, no. 1, pp. 1–61, 2007. 2. K. S. Yang, Z. Jiang, and J. S. Chung, “Electrophoretically Al-coated wire mesh and its application for catalytic oxidation of 1,2-dichlorobenzene,” Surf. Coatings Technol., vol. 168, no. 2–3, pp. 103–110, 2003. Figure 1

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 score0.019
Threshold uncertainty score0.312

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 head0.186
Teacher spread0.183 · 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
Published2021
Admission routes1
Has abstractyes

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