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Record W4214867911 · doi:10.1002/9783527815562.mme0027

Polymerizations in Aqueous Dispersed Media

2022· other· en· W4214867911 on OpenAlexaff
Connor A. Sanders, Michael F. Cunningham

Bibliographic record

Venuenot available
Typeother
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsQueen's University
Fundersnot available
KeywordsPolymerizationPolymerAqueous mediumAqueous solutionChemical engineeringMaterials scienceNanotechnologyYield (engineering)Polymer chemistryChemistryOrganic chemistryEngineeringComposite material

Abstract

fetched live from OpenAlex

Abstract Heterogeneous polymerizations in aqueous media remain an active area of research with an emphasis on control over polymer architecture and molecular weight. Previously established methods to perform reversible deactivation radical polymerizations have been expanded and will be covered in this article. Recent strategies employ novel catalysts, functional surfactants, and multistep processes to yield stable, and in some cases, high solids content, latexes through a variety of nonconventional chemistries. Moreover, polymerization‐induced self‐assembly has been developed to yield high‐order morphologies in aqueous dispersions in mild conditions. Photoinduced polymerization techniques have seen increasing interest owing to the “green” benefits of using light as a stimulus for polymerization. The goals in this field remain to provide control over polymerizations in aqueous dispersed media while reducing both monetary and environmental costs. This article will cover recent developments in producing water‐based polymer dispersions with control over nanoparticle morphology and functionality and macromolecular structure.

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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
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)0.0040.002

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 head0.006
GPT teacher head0.209
Teacher spread0.202 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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