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Record W3022616978 · doi:10.1021/la302276n

Effect of Hydrophobic Interactions on Volume and Thermal Expansivity as Derived from Micelle Formation

2012· article· en· W3022616978 on OpenAlexafffund
Mozhgan Nazari, Helen Y. Fan, Heiko Heerklotz

Bibliographic record

VenueLangmuir · 2012
Typearticle
Languageen
FieldChemistry
TopicSurfactants and Colloidal Systems
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsIsothermal titration calorimetryChemistryMicelleMethyleneThermodynamicsCalorimetryColloidAlkylVolume (thermodynamics)Hydrophobic effectCrystallographyPhysical chemistryOrganic chemistryAqueous solution

Abstract

fetched live from OpenAlex

Volumetric parameters have long been used to elucidate the phenomena governing the stability of protein structures, ligand binding, or transitions in macromolecular or colloidal systems. In spite of much success, many problems remain controversial. For example, hydrophobic groups have been discussed to condense adjacent water to a volume lower than that of bulk water, causing a negative contribution to the volume change of unfolding. However, expansivity data were interpreted in terms of a structure-making effect that expands the water interacting with the solute. We have studied volume and expansivity effects of transfer of alkyl chains into micelles by pressure perturbation calorimetry and isothermal titration calorimetry. For a series of alkyl maltosides and glucosides, the methylene group contribution to expansivity was obtained as 5 uL/(mol K) in a micelle (mimicking bulk hydrocarbon) but 27 uL/(mol K) in water (20 °C). The latter value is virtually independent of temperature and similar to that obtained from hydrophobic amino acids. Methylene contributions of micellization are about -60 J/(mol K) to heat capacity and 2.7 mL/mol to volume. Our data oppose the widely accepted assumption that water-exposed hydrophobic groups yield a negative contribution to expansivity at low temperature that would imply a structure-making, water-expanding effect.

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 score0.018
Threshold uncertainty score0.617

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 studies0.0000.000
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 head0.007
GPT teacher head0.232
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

Citations14
Published2012
Admission routes2
Has abstractyes

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