Hydrophobicity: effect of density and order on water's rotational slowing down
Bibliographic record
Abstract
Ab initio molecular dynamics (AIMD) simulations of over 4.5 ns were performed in the temperature range of T = 260-350 K with van der Waals corrections to investigate the relationship between local water density and tetrahedral order in bulk water and in the presence of a hydrophobe, tetramethylurea (TMU). We demonstrate that in bulk water, defects consisting of 5- and higher coordinated water are a major contributor to dynamics. Close to a hydrophobe, 3-coordinated defects take over. The co-existence of these defects gives rise to very different local densities. We propose that the slowing down of rotational motion close to a hydrophobe is induced by an interplay between density and order with the slowing down decreasing in the following order: (i) low-density ordered-water, (ii) normal-density ordered-water, (iii) high-density ordered-water and (iv) disordered-water. The proportions of these water environments vary with temperature. These local environments also support the idea of water's polymorphism, i.e., the existence of the high- and low-density states in supercooled water.
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 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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".