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Record W2533791567 · doi:10.1039/c6tb02270g

Supramolecular hydrogelation with bile acid derivatives: structures, properties and applications

2016· article· en· W2533791567 on OpenAlexafffund
Meng Zhang, Satu Strandman, Karen C. Waldron, X. X. Zhu

Bibliographic record

VenueJournal of Materials Chemistry B · 2016
Typearticle
Languageen
FieldMaterials Science
TopicSupramolecular Self-Assembly in Materials
Canadian institutionsUniversité de Montréal
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsSelf-healing hydrogelsAmphiphileMicelleAqueous solutionNanofiberSupramolecular chemistryCationic polymerizationBile acidDrug deliveryDissolutionMaterials scienceChemistryMoleculeChemical engineeringPolymer chemistryNanotechnologyOrganic chemistryCopolymerPolymerBiochemistry

Abstract

fetched live from OpenAlex

Hydrogelation of small molecules in aqueous solutions results from a balance between solubilization and precipitation (or crystallization). The hydrophobic moieties of amphiphiles tend to aggregate and the hydrophilic units may stabilize the aggregates in aqueous solutions. Morphologies vary according to the chemical structure of the amphiphiles. The formation of nanofibers or worm-like micelles is a prerequisite for hydrogels. Molecular hydrogels often show better degradability and functional diversity than polymeric hydrogels and may be useful in biomedical applications. Bile acids have attracted increasing attention for designing various biomaterials, including molecular hydrogels. They are naturally occurring amphiphilic compounds that exist in our body and help with the dissolution and digestion of fat by the formation of micelles. This review highlights the recent progress in the field of molecular hydrogelators based on bile acids, including bile salts, anionic, cationic and neutral bile acid derivatives, two-component hydrogelation systems, and polymeric supramolecular hydrogels, along with their potential applications.

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)0.001
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 score0.003
Threshold uncertainty score0.748

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.011
GPT teacher head0.212
Teacher spread0.201 · 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

Citations59
Published2016
Admission routes2
Has abstractyes

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