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Record W2130708717 · doi:10.1039/c0cs00149j

Electrografting: a powerful method for surface modification

2011· review· en· W2130708717 on OpenAlexaff
Daniel Bélanger, Jean Pinson

Bibliographic record

VenueChemical Society Reviews · 2011
Typereview
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsReagentOxidizing agentPolymerElectrochemistrySubstrate (aquarium)Chemical engineeringElectron transferSurface modificationRedoxLayer (electronics)Materials scienceChemistryNanotechnologyElectrodeOrganic chemistry

Abstract

fetched live from OpenAlex

Electrografting refers to the electrochemical reaction that permits organic layers to be attached to solid conducting substrates. This definition can be extended to reactions involving an electron transfer between the substrate to be modified and the reagent, but also to examples where a reducing or oxidizing reagent is added to produce the reactive species. These methods are interesting as they provide a real bond between the surface and the organic layer. Electrografting applies to a variety of substrates including carbon, metals and their oxides, but also dielectrics such as polymers. Since the 1980s several methods have been developed, either by reduction or oxidation, and some of them have reached an industrial stage. This critical review describes the methods that are used for electrografting, their mechanism, the formation and growth of the layers as well as their applications (742 references).

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.005

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 head0.183
GPT teacher head0.425
Teacher spread0.242 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations935
Published2011
Admission routes1
Has abstractyes

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