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Record W3166169249 · doi:10.3390/polym13121931

In-Situ One-Step Direct Loading of Agents in Poly(acrylic acid) Coating Deposited by Aerosol-Assisted Open-Air Plasma

2021· article· en· W3166169249 on OpenAlexafffund
Gabriel Morand, Pascale Chevallier, Cédric Guyon, Michaël Tatoulian, Diego Mantovani

Bibliographic record

VenuePolymers · 2021
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsAgence Nationale de la Recherche
KeywordsAerosolCoatingAqueous solutionChemical engineeringMaterials scienceAcrylic acidDeposition (geology)WaferChromatographyNanotechnologyOrganic chemistryChemistryComposite materialPolymerCopolymer

Abstract

fetched live from OpenAlex

In biomaterials and biotechnology, coatings loaded with bioactive agents are used to trigger biological responses by acting as drug release platforms and modulating surface properties. In this work, direct deposition of poly(acrylic acid) coatings containing various agents, such as dyes, fluorescent molecules, was achieved by aerosol-assisted open-air plasma. Using an original precursors injection strategy, an acrylic acid aerosol was loaded with an aqueous aerosol and deposited on silicon wafers. Results clearly showed that agents dissolved in the aqueous aerosol were successfully entrapped in the final coating. The effect of aerosols concentration, flow rate, and treatment time, on the coating morphology and the amount of entrapped agents, was also investigated. It was demonstrated that this process has the potential to entrap a tunable amount of any sensible water-soluble agent without altering its activity. To the best of our knowledge, this is the first time that the loading of an aqueous aerosol in coatings deposited by plasma from a liquid aerosol precursor is reported. This innovative approach complements plasma deposition of coatings loaded with bioactive agents from aqueous aerosols with the use of non-volatile liquid precursors.

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 score0.006
Threshold uncertainty score0.821

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.044
GPT teacher head0.287
Teacher spread0.243 · 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

Citations9
Published2021
Admission routes2
Has abstractyes

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