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Record W7064451785

Ceramic coatings on non-valve metals deposited by plasma electrolysis

2021· dissertation· en· W7064451785 on OpenAlexfundno aff

Bibliographic record

VenueScholarship at UWindsor (University of Windsor) · 2021
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicAstrophysical Phenomena and Observations
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of WindsorFord Motor Company
KeywordsElectrolyteCorrosionCoatingElectrolysisDuty cycleSubstrate (aquarium)Indentation hardnessPlasma electrolytic oxidation
DOInot available

Abstract

fetched live from OpenAlex

A modified plasma electrolytic oxidation (PEO) treatment has been successfully developed for the non-valve metals of Fe and Cu in electrolyte containing sodium aluminate and sodium phosphate. This process could also be termed as plasma electrolytic aluminating (PEA) since the formation of passive films mainly relies on the aluminate ions. The passive film will hinder the current flow and cause charge build-up. When a critical voltage is reached, dielectric breakdown of the passive film will ignite the sparks. X-ray photoelectron spectroscopy (XPS) analyses indicate the passive film formed on the Fe consist of FeAl2O4, which means iron substrate participated in the reaction. On the other hand, the copper substrate was not involved in the passive film formed on the Cu, which consists of Al(OH)3. The different mechanisms could be attributed to the different reduction potentials of Fe and Cu. Taguchi analyses were used to investigate the influence of selected process parameters, including the concentration of NaAlO2 in the electrolyte (C), the frequency (f) and duty cycle (δ) of the power supply. ANOVA analysis revealed that C has the most significant contribution to hardness, corrosion resistance and thickness. While f has significant influence on hardness and corrosion resistance, δ contributes significantly to the thickness. Higher frequency means shorter duration of a single discharge which leads to denser coating with higher hardness and corrosion resistance. Higher duty cycle represents the higher power input during the PEA treatment. Therefore, the coating’s thickness increased with higher duty cycle. The coating prepared on iron substrate mainly consists of Al2O3 and FeAl2O4. The hardness, polarization resistance and thermal conductivity of the coating were 822 HV, 296 kΩ·cm2 and ~0.5 W/(m·K), respectively. The low thermal conductivity comes from the mesopores, nano-grains and amorphous materials. After cyclic thermal shock tests, the coating retained its porous structure without spallation. Post-treatments like electroless nickel plating (EP) and sol-gel silica coating were applied to seal the open pores and cracks. Both the PEA-EP and PEA-SiO2 hybrid coatings could retain good corrosion resistance after immersed in sodium chloride solution for five days, while the PEA coating degraded due to pitting corrosion at these open pores and cracks. The coating deposited on pure copper consists of ceramic matrix (Al2O3 and Cu2O) embedded with Cu particles. The amount of Cu particles increased with increased coating thickness, which could be attributed to intensified plasma discharges. The hardness, polarization resistance and thermal conductivity of the coating were 1050 HV, 141.7 kΩ·cm2 and ~5.1 W/(m·K), respectively. The increased thermal conductivity could be attributed to the presence of metallic Cu. The coating has excellent wear and corrosion resistance, which might be used for wear-corrosion protection of copper alloys.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.008
GPT teacher head0.213
Teacher spread0.205 · 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 source (direct Gemma or distilled Codex), 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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