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Record W2794196634 · doi:10.1002/ppap.201800002

The effect of sterilization procedures on the physiochemical properties and performance of plasma polymer films

2018· article· en· W2794196634 on OpenAlexafffund
Sara Babaei, А. А. Касимов, Pierre‐Luc Girard‐Lauriault

Bibliographic record

VenuePlasma Processes and Polymers · 2018
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSterilization (economics)Plasma polymerizationMaterials sciencePolymerChemical engineeringEthylene oxideX-ray photoelectron spectroscopyContact anglePolymerizationHydrogen sulfidePolymer chemistryComposite materialCopolymerMetallurgySulfur

Abstract

fetched live from OpenAlex

Sterilization procedures can alter the desirable characteristics brought by plasma polymerization. Different plasma‐deposited functional organic coatings are prepared by plasma co‐polymerization of binary gas mixtures constituted of a hydrocarbon (butadiene/ethylene) and a heteroatom containing gas (carbon dioxide/ammonia/hydrogen sulfide). These coatings are then treated by dry heating, autoclaving, ethylene oxide, UV, and gamma‐ray irradiation. The physio‐chemical properties and performance of the samples are evaluated by profilometry, water contact angle goniometry, XPS, and 1‐h adhesion tests with U937 cells before and after sterilization. The results reveal that the changes during these sterilization processes are dependent on the type of plasma polymer and on the sterilization method. We derive recommendations on the suitability of the sterilization methods for the different coatings.

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)0.001
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.016
GPT teacher head0.220
Teacher spread0.204 · 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

Citations1
Published2018
Admission routes2
Has abstractyes

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