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Record W2318596440 · doi:10.1021/mz2000175

General Strategy for Making CO<sub>2</sub>-Switchable Polymers

2011· article· en· W2318596440 on OpenAlexafffund
Dehui Han, Xia Tong, Olivier Boissière, Yue Zhao

Bibliographic record

VenueACS Macro Letters · 2011
Typearticle
Languageen
FieldChemical Engineering
TopicCarbon dioxide utilization in catalysis
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLower critical solution temperatureCopolymerMethacrylatePolymerMaterials scienceSelf-healing hydrogelsMicellePolymer chemistrySolubilityChemical engineeringSmart polymerThermoresponsive polymers in chromatographyMonomerAqueous solutionChemistryOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

By discovering that poly( N, N -dimethylaminoethyl methacrylate) (PDMAEMA) in water can react with carbon dioxide (CO 2 ) and have its lower critical solution temperature (LCST) reversibly tuned by passing CO 2 and argon (Ar) through the solution, we describe a general strategy for imparting a CO 2 -switchable LCST or water solubility to polymers of broad interest like poly( N -isopropylacrylamide) (PNIPAM) and poly[2-(2-methoxyethoxy)ethyl methacrylate] (PMEO 2 MA). We show that by easy copolymerization incorporating DMAEMA as a CO 2 -responsive trigger into PNIPAM or PMEO 2 MA, their LCST can effectively be switched by the gases. Two examples of applications were further demonstrated: upon CO 2 or Ar bubbling at a constant solution temperature, hydrogels could undergo a reversible volume transition and block copolymer micelles could be dissociated and reassembled. This study opens the door to a wide range of easily accessible CO 2 -switchable polymers, enabling the use of CO 2 as an effective trigger for smart materials and devices.

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 score0.002
Threshold uncertainty score0.005

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.029
GPT teacher head0.254
Teacher spread0.225 · 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

Citations270
Published2011
Admission routes2
Has abstractyes

Explore more

Same venueACS Macro LettersSame topicCarbon dioxide utilization in catalysisFrench-language works237,207