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Record W2025880340 · doi:10.1021/jp0643451

Kinetic Prediction of Functional Group Distributions in Thermosensitive Microgels

2006· article· en· W2025880340 on OpenAlexaff
Todd Hoare, Daniel G. McLean

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHydrogels: synthesis, properties, applications
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCopolymerMonomerPotentiometric titrationKinetic energyKineticsPolymerizationPolymer chemistryMaterials scienceFunctional groupChemistryTitrationCarboxylic acidThermodynamicsPhysical chemistryPolymerOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

A kinetic model accounting for the copolymerization of up to four comonomers is applied to predict both chain and radial functional group distributions in carboxylic-acid-functionalized poly(N-isopropylacrylamide) (NIPAM)-based microgels. The model can accurately predict the experimentally observed radial distributions of functional monomers in microgels prepared using a variety of different carboxylic-acid-functionalized monomers with significantly different hydrophobicities, copolymerization kinetics, and reactivities, without requiring the use of adjustable parameters. Multimodal distributions can both be predicted and experimentally generated by copolymerizing two -COOH-containing monomers with widely different reactivities. Chain distributions and monomer block formation can also be probed using the kinetic model, allowing for qualitative predictions of the potentiometric titration behavior of the microgels. The kinetic model reported herein therefore provides the first available analytical method for semiquantitatively predicting and controlling functional group distributions in bulk-polymerized microgel systems.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.012
Threshold uncertainty score0.300

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.009
GPT teacher head0.209
Teacher spread0.200 · 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 teacher head, 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

Citations116
Published2006
Admission routes1
Has abstractyes

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