Effect of Hydrophobic Interactions on Volume and Thermal Expansivity as Derived from Micelle Formation
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".