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Understanding the effect of surface topography on the formation of aerosol-deposited coatings

2025· article· en· W4406788448 on OpenAlexafffund
Zhenying Yang, S. Rahmati, Ali Dolatabadi, Thomas W. Coyle

Bibliographic record

VenueCeramics International · 2025
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaConcordia UniversityMcMaster University
KeywordsMaterials scienceAerosolSurface (topology)Chemical engineeringNanotechnologyChemical physicsComposite materialMineralogyMeteorologyGeometryGeologyChemistry

Abstract

fetched live from OpenAlex

Aerosol deposition (AD) has attracted attention in the ceramic coating field as it can produce dense nanocrystalline ceramic films at room temperature. Research in the AD field has focused on coating formation on flat surfaces, with limited exploration of deposition on patterned substrates. In this work, we describe the microstructure of a dense alumina coating deposited by AD on micropillar-patterned silicon substrates through SEM and TEM analyses. To understand the coating formation mechanism on the micro-patterned substrate, interfacial analysis has been performed using high-resolution TEM. Pressure-induced amorphous Si regions are observed at the coating-substrate interfaces, indicating severe particle impacts during deposition that contribute to the strong adhesive bonding in AD. The interfaces at different locations, including the peaks, sidewalls, and valleys of the pillars, showed distinct characteristics that demonstrated how substrate topography in AD affected interfacial bonding and particle impact behaviors. • Alumina films were fabricated on patterned Si substrates by aerosol deposition. • The coatings deposited between the pillars show high density without microcracks. • Thick amorphous Si layer is seen at coating-substrate interfaces near pillar peaks. • The particle impact behaviours are explained through HRTEM analysis.

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 score0.211
Threshold uncertainty score0.234

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 head0.018
GPT teacher head0.245
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.

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 routes2
Has abstractyes

Explore more

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