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Record W2030909676 · doi:10.1002/mren.200900063

Kinetic Modeling of Surface‐Initiated Atom Transfer Radical Polymerization

2010· article· en· W2030909676 on OpenAlexafffund
Xiang Gao, Wei Feng, Shiping Zhu, Heather Sheardown, John L. Brash

Bibliographic record

VenueMacromolecular Reaction Engineering · 2010
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAtom-transfer radical-polymerizationCatalysisRadical polymerizationPolymerPolymer chemistryPolymerizationChemistryWaferChemical engineeringMaterials scienceNanotechnologyOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract A kinetic model has been developed using the method of moment for surface‐initiated atom transfer radical polymerization (s‐ATRP) from flat solid surfaces based on a moving boundary physical model. The model is verified with the experimental data of 2‐methacryloyloxyethyl phosphorylcholine from silicon wafer, which were carried out by either adding free initiator (Method I) or excess deactivator (Method II) to the solution. It is shown through the modeling that Method II gives better control over polymer molecular weight and thicker graft layer under similar conditions than Method I. A new mechanism is proposed for the radical termination based on the fact that the rapid activation/deactivation cycle reactions facilitate “migration” of radical centers on the surface. The rate constant of “migration termination” is thus catalyst concentration dependent with higher catalyst concentration resulting in higher termination. Lowering catalyst concentration suppressed migration termination that could improve the control and livingness of s‐ATRP. However, there exists a catalyst concentration for the optimal control performance. magnified image

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.008
GPT teacher head0.206
Teacher spread0.198 · 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 designSimulation or modeling
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

Citations45
Published2010
Admission routes2
Has abstractyes

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