Influence of Urea on <i>tert</i>-Butyl Alcohol Aggregation in Aqueous Solutions
Bibliographic record
Abstract
Ternary solutions consisting of urea, tert-butyl alcohol (TBA), and water are investigated employing molecular dynamics simulations. The main purpose of the present paper is to investigate the effect of urea on TBA aggregation and by extension its influence on hydrophobic interactions. The aggregation of TBA can be detected from the concentration dependence of structural properties such as first-shell TBA-water coordination numbers and TBA-TBA hydrogen-bond numbers, as well as through changes in the translational diffusion coefficients of TBA. It is found that urea acts to delay the association of TBA to concentrations greater than those required to cause TBA aggregation in binary TBA-water systems. It is shown that urea acts through a direct mechanism, whereby it preferentially binds to TBA replacing water from the first coordination shell. TBA-urea hydrogen bonds can be as strong as, or stronger than, those of TBA-water, and urea binds to both the hydrophilic and hydrophobic moieties of TBA. Our observations are qualitatively consistent with experimental results for urea-TBA-water solutions and with recent simulation studies of urea's action as a protein denaturant.
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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 | 0.000 | 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 | 0.000 |
| 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".