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Record W4403983214 · doi:10.1116/6.0003954

Robust coating for high-temperature and corrosion-resistant

2024· article· en· W4403983214 on OpenAlexaff
Xing Shen, Xuhong Xu, Chenshi Li, Jingjing Wang, Alidad Amirfazli

Bibliographic record

VenueJournal of Vacuum Science & Technology A Vacuum Surfaces and Films · 2024
Typearticle
Languageen
FieldMaterials Science
TopicMaterial Properties and Applications
Canadian institutionsYork University
FundersNational Natural Science Foundation of China
KeywordsCorrosionCoatingMaterials scienceMetallurgyComposite material

Abstract

fetched live from OpenAlex

Polysilazane (PSZ) is a class of novel materials with significant advantages; however, its practical applications are severely limited due to drawbacks such as the need for high-temperature curing and susceptibility to brittleness and cracking. Consequently, we have chosen PSZ and epoxy resin (EP) as the film-forming resins, with silica aerogel (SiO2gel) serving as the inorganic filler, to fabricate a SiO2gel–PSZ/EP composite coating capable of curing at room temperature. The incorporation of EP and SiO2gel has improved the toughness, mechanical stability, and thermal stability of PSZ. After 800 cycles of abrasion wear, the composite coating maintained its surface integrity. The scratch test rated its adhesion at level 1. Additionally, after 14 days of immersion in acidic and alkaline solutions, the coating demonstrated favorable chemical stability. The coating underwent 10 cycles of thermal shock testing, during which no significant cracking or peeling was observed on the surface. Finally, electrochemical impedance spectroscopy testing revealed that, after exposure to 300 °C, the composite coating exhibited a corrosion current density of 1.23 × 10−10 cm2, corresponding to a corrosion protection efficiency of up to 99.99%. In summary, the coating maintains excellent anticorrosion properties even after exposure to high temperatures and demonstrates outstanding stability, significantly enhancing its durability in harsh environments. This enhancement suggests a broad potential for applications in the field of subsea transportation.

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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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 head0.016
GPT teacher head0.248
Teacher spread0.232 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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