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Record W2901417898 · doi:10.1002/app.47284

Drug release kinetics of pH‐responsive microgels of different glass‐transition temperatures

2018· article· en· W2901417898 on OpenAlexafffund
Muhammad Shahidul Islam, Jeremy P. K. Tan, Chun Yuen Kwok, Kam Chiu Tam

Bibliographic record

VenueJournal of Applied Polymer Science · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHydrogels: synthesis, properties, applications
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaAgency for Science, Technology and ResearchMinistry of Education
KeywordsMethacrylic acidGlass transitionPolymer chemistryMethacrylateEmulsion polymerizationChemistrySwellingEthyl acrylateDiffusionRadical polymerizationPoly(methacrylic acid)KineticsChemical engineeringPolymerizationMaterials scienceNuclear chemistryPolymerMethyl methacrylateOrganic chemistry

Abstract

fetched live from OpenAlex

ABSTRACT The pH‐responsive microgels (MGs) consisting of methacrylic acid‐ethyl acrylate (MAA‐EA), methacrylic acid‐butyl methacrylate (MAA‐BMA) or methacrylic acid‐methyl methacrylate (MAA‐MMA) crosslinked with di‐allyl phthalate (DAP) were synthesized via emulsion polymerization. It was found that the energy required to extract a proton from MGs with higher glass‐transition temperature ( T g ) was greater than at a lower T g . Procaine hydrochloride (PrHy) was used to study the release of a model hydrophobic drug from MGs with different T g s. A drug selective electrode (DSE) was used to monitor the release as a function of pHs and T g s. With increasing pH or decreasing T g , the swelling of MGs was enhanced, leading to greater release of the drug. From the Berens and Hopfenberg model, the contributions of chain relaxation and diffusion processes during a release process were determined. The drug release from lower T g MGs and at high pH is dominated by diffusion rather than chain relaxation. © 2018 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136 , 47284.

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 score0.002
Threshold uncertainty score0.435

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.007
GPT teacher head0.233
Teacher spread0.226 · 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

Citations4
Published2018
Admission routes2
Has abstractyes

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