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Record W2041055673 · doi:10.1002/pola.20095

Atom‐transfer radical grafting polymerization of 2‐methacryloyloxyethyl phosphorylcholine from silicon wafer surfaces

2004· article· en· W2041055673 on OpenAlexaff
Wei Feng, John L. Brash, Shiping Zhu

Bibliographic record

VenueJournal of Polymer Science Part A Polymer Chemistry · 2004
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Surface Interaction Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAtom-transfer radical-polymerizationMonomerPolymer chemistryGraftingPolymerizationMaterials scienceWaferMethacrylateRadical polymerizationChemical engineeringChemistryPolymerComposite materialNanotechnology

Abstract

fetched live from OpenAlex

Abstract 2‐Methacryloyloxyethyl phosphorylcholine (MPC), a biomimetic monomer, was grafted from silicon wafer surfaces at room temperature by combining self‐assembly of initiator and surface‐initiated atom transfer radical polymerization. Two methods were used to control the grafting process. One was to add free initiator to the reaction system; the other was to add excess deactivator. The grafting densities up to 0.3 chains/nm 2 were obtained. The surface thickness increased linearly with MPC conversion. The thickness depended on catalyst and monomer concentrations, as well as activator/deactivator ratio. Poly(MPC) layers of >100 nm thick were obtained by optimizing the polymerization conditions. A second block of either poly(MPC) or poly[2‐(dimethylamino)ethyl methacrylate] was also grown from the grafted poly(MPC), demonstrating the system livingness. X‐ray photoelectron spectroscopy was used to examine the surface chemical compositions showed good agreement with the theoretical values. © 2004 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem 42: 2931–2942, 2004

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 communication0.0000.000
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 head0.014
GPT teacher head0.267
Teacher spread0.253 · 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

Citations157
Published2004
Admission routes1
Has abstractyes

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