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Record W4387574916 · doi:10.32865/2346/94591

Coatings for Space-Based Systems: Impacts of Plasma Processes

2023· preprint· en· W4387574916 on OpenAlexfundno aff
Richard Clergereaux, Veronica Orlandi, Myrtil L. Kahn, Grégory Navarro, Alexis Paillet

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMaterials Science
TopicSilicone and Siloxane Chemistry
Canadian institutionsnot available
FundersUniversité de MontréalCentre National de la Recherche ScientifiqueUniversité de Toulouse
KeywordsPlasmaSpace (punctuation)Astrophysical plasmaComputer scienceAerospace engineeringMaterials scienceEnvironmental sciencePhysicsEngineeringOperating systemNuclear physics

Abstract

fetched live from OpenAlex

Plasma assisted processes have become more and more important in surface treatments applied to different domains.For example, thin metallic films, dielectric layers or hard coatings with functional properties related to the material structure and composition are widely used in different fields of applications.Nowadays, multifunctional coatings are also highly required.For example, sustainable activated filters, protective layer to reduce system ageing, as well as combined superhydrophobic, anti-fouling and resistant coatings are expected in various terrestrial applications.Nanocomposite thin films i.e. coatings of matrixembedded nanoparticles are potential candidates as they are exhibiting multifunctional properties related to the matrix and the nanoparticles and, especially, their concentration, size, shape, and distribution in the coating.Different plasma strategies are developed.This presentation aims to review different plasma assisted technologies to form innovative coatings and their potential impacts for space-based systems with a specific focus on safer-by-design methods.

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)0.001
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 score0.023
Threshold uncertainty score0.872

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.049
GPT teacher head0.291
Teacher spread0.242 · 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
Published2023
Admission routes1
Has abstractyes

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