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Record W4402154222 · doi:10.31399/asm.cp.itsc2024p0166

Understanding the Formation of Different Surface Structures of Hydrophobic Ceramic Coatings Deposited in the SPAPS Process and SPVPS Process

2024· article· en· W4402154222 on OpenAlexaff
Pengyun Xu, Xiaomu Sui, Thomas W. Coyle, Larry Pershin, J. Mostaghimi

Bibliographic record

VenueThermal spray · 2024
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsWettingMaterials scienceContact angleSubstrate (aquarium)OxideCeramicPlasmaAtmospheric-pressure plasmaChemical engineeringNanotechnologyComposite materialMetallurgy

Abstract

fetched live from OpenAlex

Abstract Surface structures are of vital importance for the wetting behaviors of hydrophobic coatings. In this work, rare earth oxide coatings with different surface structures were deposited via the solution precursor atmospheric plasma spray (SPAPS) process and solution precursor vacuum plasma spray (SPVPS) process, respectively. The SPAPS coatings showed hierarchical cauliflower-like surface structures composed of micron-sized clusters and nanometer-sized particles, while the SPVPS coatings showed relatively flat topographies with small and short bumps. The formation of different surface structures in the SPAPS and SPVPS processes was investigated by modelling the movement of in-flight particles in the vicinity of the substrate. The properties of plasma jets and the characteristics of in-flight particles in the two processes were correlated. The effects of diverted plasma gas flow on the trajectories of particles impinging on the substrate and the resultant surface structures were elaborated, revealing different shadowing effects in the SPAPS and SPVPS processes. The SPAPS coatings were superhydrophobic due to the presence of hierarchical surface structures, which showed larger water contact angles and smaller roll-off angles than the SPVPS coatings. The correlations between the surface structures and wetting behaviors of different coatings were investigated.

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 score0.014
Threshold uncertainty score0.250

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 head0.040
GPT teacher head0.273
Teacher spread0.233 · 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
Published2024
Admission routes1
Has abstractyes

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