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Record W2137571927 · doi:10.3390/polym5041229

Surface Initiated Polymerizations via e-ATRP in Pure Water

2013· article· en· W2137571927 on OpenAlexafffund
Seyed Schwan Hosseiny, Patrick van Rijn

Bibliographic record

VenuePolymers · 2013
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Surface Interaction Studies
Canadian institutionsConcordia University
FundersForeign Affairs and International Trade CanadaAlexander von Humboldt-Stiftung
KeywordsAtom-transfer radical-polymerizationQuartz crystal microbalanceElectrochemistryMaterials scienceSurface modificationChemical engineeringPolymerizationElectrolyteElectrodeCatalysisPolymerAqueous solutionPolymer chemistryAdsorptionChemistryOrganic chemistryComposite materialPhysical chemistry

Abstract

fetched live from OpenAlex

Here we describe the combined process of surface modification with electrochemical atom transfer radical polymerization (e-ATRP) initiated from the surface of a modified gold-electrode in a pure aqueous solution without any additional supporting electrolyte. This approach allows for a very controlled growth of the polymer chains leading towards a steady increase in film thickness. Electrochemical quartz crystal microbalance displayed a highly regular increase in surface confined mass only after the addition of the pre-copper catalyst which is reduced in situ and transformed into the catalyst. Even after isolation and washing of the modified electrode surface, reinitiation was achieved with retention of the controlled electrochemical ATRP reaction. This reinitiation after isolation proves the livingness of the polymerization. This approach has interesting potential for smart thin film materials and offers also the possibility of post-modification via additional electrochemical induced reactions.

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 score0.001
Threshold uncertainty score0.005

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.023
GPT teacher head0.269
Teacher spread0.246 · 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

Citations29
Published2013
Admission routes2
Has abstractyes

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