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Record W4281263376 · doi:10.1039/d2tb00364c

Design of hydrogel–microgel composites with tailored small molecule release profiles

2022· article· en· W4281263376 on OpenAlexafffund
Siyuan Guo, Daniel J. Wong, Sifan Wang, R.S. Gill, Michael J. Serpe

Bibliographic record

VenueJournal of Materials Chemistry B · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHydrogels: synthesis, properties, applications
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaGrand Challenges CanadaCanada Foundation for InnovationUniversity of Alberta
KeywordsMaterials scienceComposite materialSelf-healing hydrogelsMoleculePolymer chemistryChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

electrostatics, the HMC was able to release the CV in a pH-triggered fashion. We found that the CV release rate was dependent on the solution temperature and the dimension of the material. Also, by changing the chemical composition and/or pore size of the hydrogel matrix, the CV release kinetics can be tuned. Moreover, when multiple microgels loaded with different model drugs were embedded in a single HMC, the HMC can be used to control the release rate of each drug analog individually in a pH-dependent fashion. By understanding how properties of a hydrogel can alter the release of small molecules from embedded microgels, new materials capable of controlled and triggered release of multiple small molecule drugs can be designed with myriad uses in the biomedical field.

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 score0.005

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 head0.014
GPT teacher head0.210
Teacher spread0.196 · 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

Citations5
Published2022
Admission routes2
Has abstractyes

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