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Record W4288061482 · doi:10.1002/ppap.202100252

Aerosol‐assisted open‐air plasma deposition of acrylate‐based composite coatings: Molecule release control through precursor selection

2022· article· en· W4288061482 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePlasma Processes and Polymers · 2022
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAcrylateMaterials scienceChemical engineeringComposite numberMethacrylic acidAcrylic acidControlled releasePolymer chemistryDeposition (geology)PolymerCopolymerComposite materialNanotechnology

Abstract

fetched live from OpenAlex

Abstract Aerosol‐assisted open‐air plasma allows the direct deposition of composite coatings with embedded bioactive agents. However, the deposition of biodegradable coatings for controlled drug release application remains challenging. In this study, an innovative precursor injection strategy is used to entrap tracers in coatings deposited from various acrylate‐based precursors: Acrylic acid (AA), methacrylic anhydride (MA), and 1,4‐butanediol diacrylate (BDDA). Water immersion tests show dramatically different release kinetics ranging from over minutes to weeks and months for AA, MA, and BDDA, respectively. These different behaviors are correlated to the crosslinking degree and the hydrolysis rates of the crosslinking functions. This original approach allows controlled release of tracers entrapped in biodegradable acrylate‐based coatings and provides new insights for drug‐release application.

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 score0.014
Threshold uncertainty score0.793

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 studies0.0010.000
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 head0.019
GPT teacher head0.254
Teacher spread0.235 · 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