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Record W2561718813 · doi:10.1021/acs.langmuir.6b04033

Self-Assembly of a Bile Acid Dimer in Aqueous Solutions: From Nanofibers to Nematic Hydrogels

2017· article· en· W2561718813 on OpenAlexafffund
Meng Zhang, Colin Fives, Karen C. Waldron, X. X. Zhu

Bibliographic record

VenueLangmuir · 2017
Typearticle
Languageen
FieldMaterials Science
TopicSupramolecular Self-Assembly in Materials
Canadian institutionsUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship CouncilFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsSelf-healing hydrogelsNanofiberAqueous solutionLiquid crystalDimerChemical engineeringMaterials sciencePolymer chemistryChemistryOrganic chemistryNanotechnology

Abstract

fetched live from OpenAlex

A mixture of a cholic acid dimer with a secondary amine group and formic acid at a molar ratio of 1/1 is regarded as an organic salt, and it self-assembles in aqueous solutions to form monodisperse nanofibers. The nanofibers are separated at low concentrations of the mixture but entangle with each other at high concentrations to form well-dispersed and randomly arranged 3D fibrous networks. Above the minimum gelation concentration of the dimer, the fibrous network is strong enough to gelate the aqueous solutions to form a hydrogel. Hydrogels obtained from the dimer salt at a lower concentration are isotropic and show extinction between crossed polarizers in the polarizing microscope, whereas they become anisotropic (i.e., nematic hydrogels) upon increasing the dimer salt concentration or under physical stirring. The parallel arrangement of nanofibers from randomly directed fibrous networks may be responsible for the formation of such nematic hydrogels.

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 score0.002

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 head0.019
GPT teacher head0.272
Teacher spread0.253 · 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

Citations25
Published2017
Admission routes2
Has abstractyes

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