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Record W2912054898 · doi:10.1039/c8ob02909a

Using reversible non-covalent and covalent bonds to create assemblies and equilibrating molecular networks that survive 5 molar urea

2019· article· en· W2912054898 on OpenAlexafffund
Meagan A. Beatty, Aidan T. Pye, Alok Shaurya, Belim Kim, Allison J. Selinger, Fraser Hof

Bibliographic record

VenueOrganic & Biomolecular Chemistry · 2019
Typearticle
Languageen
FieldMaterials Science
TopicSupramolecular Self-Assembly in Materials
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCovalent bondChemistryUreaMolarMolar ratioOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

The limits of self-assembly and host-guest chemistry in water solutions containing competitive solutes are largely unexplored. We report here a new family of self-assembling systems that are stitched together at two levels by reversible hydrazone bonds and by non-covalent self-assembly in strongly denaturing conditions. Three different hydrazides of various charge and hydrophobicity are combined with an aldehyde-containing calixarene, and each system spontaneously forms AB hydrazones that subsequently self-assemble into four-component (AB)2 structures in water. The assemblies display varying responses to added NaCl and/or urea. The most robust assembly survives completely intact in solution up to 5 M urea. We also combine the aldehyde calixarene with two different hydrazides in the same tube to create complex, competitive dynamic libraries. We report experiments in which the composition of the dynamic equilibrating library is under the control of self-assembly, allowing the systems to choose the components that form the most stable assemblies under a variety of competitive solutions conditions. These dynamic networks of equilibrating molecules maintain remarkably similar equilibrium positions under widely varying concentrations of urea and NaCl.

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 categoriesMeta-epidemiology (narrow)
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 score0.064
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.013
GPT teacher head0.247
Teacher spread0.234 · 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.

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

Citations8
Published2019
Admission routes2
Has abstractyes

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