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Record W2801361984 · doi:10.1002/ppap.201700214

Low‐pressure plasma treatment for direct amination of L605 CoCr alloy for the further covalent grafting of molecules

2018· article· en· W2801361984 on OpenAlexafffund
Sergio Diaz‐Rodriguez, Pascale Chevallier, Diego Mantovani

Bibliographic record

VenuePlasma Processes and Polymers · 2018
Typearticle
Languageen
FieldEngineering
TopicMetal and Thin Film Mechanics
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiomoleculeGraftingAminationCovalent bondMaterials scienceX-ray photoelectron spectroscopyNanotechnologySurface modificationChemical engineeringSubstrate (aquarium)Reductive aminationDeposition (geology)MetalAlloyHomogeneousMoleculePlasmaChemistryOrganic chemistryComposite materialMetallurgyCatalysisPolymer

Abstract

fetched live from OpenAlex

Low‐pressure plasma represents a versatile platform for material surface modifications. As regards metallic substrates, the deposition of polymeric coatings generally constitutes an added step prior to biomolecule grafting. An original multi‐step approach involving surface preparation, direct plasma‐amination, and biomolecule grafting was explored in the present study. Overall results, supported by XPS and ToF‐SIMS analyses, confirm the successful, and homogeneous grafting of peptide onto the metallic substrate. To the best of our knowledge, this is the first study to present a successful low‐pressure plasma amination procedure in metal. This innovative technique has the potential to easily graft biomolecules and thus creates opportunities for advances in the development of novel functional devices.

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 score0.002
Threshold uncertainty score0.006

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)0.0020.001

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 head0.015
GPT teacher head0.229
Teacher spread0.214 · 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

Citations6
Published2018
Admission routes2
Has abstractyes

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