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Record W2041818771 · doi:10.1021/ma051685w

Polymer Interdiffusion vs Cross-Linking in Carboxylic Acid−Carbodiimide Latex Films. Effect of Annealing Temperature, Reactive Group Concentration, and Carbodiimide Substituent

2006· article· en· W2041818771 on OpenAlexaff
Hung H. Pham, Mitchell A. Winnik

Bibliographic record

VenueMacromolecules · 2006
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCarbodiimidePolymer chemistryPolymerSubstituentChemistryMethacrylateDiffusionChemical engineeringOrganic chemistryPolymerization

Abstract

fetched live from OpenAlex

This article describes the results of experiments examining the competition between the polymer interdiffusion and a cross-linking reaction in poly(2-ethylhexyl methacrylate) latex blend films in which the reactive groups (carboxylic acid and carbodiimide groups) are present in separate latex particles. In this system, polymer diffusion is necessary to bring the reactive groups into proximity, but the reaction creates long chain branches and gel that retard or inhibit further polymer diffusion. To enable measuring polymer diffusion rates by the energy transfer method, the methacrylic acid-containing particles were also labeled with a donor dye, and the −NCN-containing particles were labeled with an acceptor dye. Along with the extent of polymer diffusion, we monitored the growth in gel content as well as the consumption of carbodiimide groups. High gel content in the final film was promoted by factors that maximize the rate of polymer diffusion relative to the rate of the cross-linking reaction: high annealing temperature, low functional group content, and less reactive carbodiimide groups.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
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)0.0010.000

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 head0.003
GPT teacher head0.222
Teacher spread0.219 · 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

Citations28
Published2006
Admission routes1
Has abstractyes

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