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Record W2788772105 · doi:10.1155/2018/4749501

Nanocomposite Coatings: Preparation, Characterization, Properties, and Applications

2018· article· en· W2788772105 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Corrosion · 2018
Typearticle
Languageen
FieldEngineering
TopicMetal and Thin Film Mechanics
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsNanocompositeMaterials scienceCoatingCorrosionCharacterization (materials science)BiofoulingPorosityNanocrystalline materialComposite materialNanotechnologyMembrane

Abstract

fetched live from OpenAlex

Incorporation of nanofillers into the organic coatings might enhance their barrier performance, by decreasing the porosity and zigzagging the diffusion path for deleterious species. Thus, the coatings containing nanofillers are expected to have significant barrier properties for corrosion protection and reduce the trend for the coating to blister or delaminate. On the other hand, high hardness could be obtained for metallic coatings by producing the hard nanocrystalline phases within a metallic matrix. This article presents a review on recent development of nanocomposite coatings, providing an overview of nanocomposite coatings in various aspects dealing with the classification, preparative method, the nanocomposite coating properties, and characterization methods. It covers potential applications in areas such as the anticorrosion, antiwear, superhydrophobic area, self-cleaning, antifouling/antibacterial area, and electronics. Finally, conclusion and future trends will be also reported.

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.

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 score0.415
Threshold uncertainty score0.231

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 head0.013
GPT teacher head0.226
Teacher spread0.213 · 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