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Features of carbon nanotubes deposition on a polymer substrate by cold gas-dynamic spray technique [in English]

2025· article· en· W4414652710 on OpenAlexaff
Олександр Володимирович Гондлях, Illia Yankovskyi, Sergiy Antonyuk

Bibliographic record

VenueProceedings of the NTUU “Igor Sikorsky KPI” Series Chemical engineering ecology and resource saving · 2025
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsInstitute of Particle Physics
Fundersnot available
KeywordsCarbon nanotubeDeposition (geology)PolymerSubstrate (aquarium)NanocompositePolymer nanocompositeParticle (ecology)

Abstract

fetched live from OpenAlex

Cold gas-dynamic spraying of carbon nanotubes onto polymer substrates is a promising technique for the formation of functional coatings. However, the process of particle deposition on polymer substrates remains insufficiently studied. In this study, a numerical model was developed to analyze the mechanisms of contact interaction between nanotubes and a polyetheretherketone substrate during deposition. The effect of particle velocity on plastic deformation, local heating, and the conditions for nanotube bonding to the polymer substrate has been investigated. The obtained results show the existence of a threshold velocity at which effective mechanical bonding occurs. The conditions for local temperature rise in the substrate, which may promote the formation of a stable nanocomposite coating, have also been analyzed. The proposed model enables the prediction of optimal deposition parameters to ensure high efficiency of nanotube deposition.

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 score0.003
Threshold uncertainty score0.009

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)0.0030.000

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 head0.002
GPT teacher head0.178
Teacher spread0.177 · 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
Published2025
Admission routes1
Has abstractyes

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Same venueProceedings of the NTUU “Igor Sikorsky KPI” Series Chemical engineering ecology and resource saving→Same topicCarbon Nanotubes in Composites→French-language works237,207→