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Record W4411109517 · doi:10.1002/nano.70021

Copolymerization of Hydrophilic and Hydrophobic Monomers in Water Assisted by Cyclodextrins

2025· article· en· W4411109517 on OpenAlexafffund
Alexy Sanseigne, Guillaume Beaudoin, Ka Ho Yau, X. X. Zhu

Bibliographic record

VenueNano Select · 2025
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsUniversité de Montréal
FundersFonds Québécois de la Recherche sur la Nature et les TechnologiesNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsCopolymerMonomerCyclodextrinPolymer chemistryHydrophobic effectChemical engineeringChemistryHydrophobeMaterials scienceOrganic chemistryPolymerBiochemistry

Abstract

fetched live from OpenAlex

ABSTRACT To facilitate the copolymerization of both hydrophobic and hydrophilic monomers in aqueous media, cyclodextrins (CDs) are introduced to form inclusion complexes with the hydrophobic monomers. As a proof of concept, β‐CD is used to solubilize a hydrophobic acrylamide derivative of cholic acid (CAAM) in water, enabling its copolymerization with acrylamide to occur in a common and benign solvent, that is, water. An excess of β‐CD is needed to effectively solubilize the cholic acid monomer CAAM in water, leading to the synthesis of a thermoresponsive polymer. The CAAM content in the final copolymer is ca. 2.5 mol%, in contrast to the 5 mol% contained in the feed. Notably, this investigation underscores the usefulness of inclusion complexation in the copolymerization process. The same monomer formed a looser inclusion complex with γ‐CD, which has a larger cavity than β‐CD, and produced a copolymer with only 0.74 mol% of CAAM, indicating the importance of the stability of complexation.

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 score0.004

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 head0.003
GPT teacher head0.206
Teacher spread0.204 · 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
Published2025
Admission routes2
Has abstractyes

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