Hydrophobicity vs Hydrophilicity: Effects of Poly(ethylene glycol) and <i>tert</i>-Butyl Alcohol on H<sub>2</sub>O as Probed by 1-Propanol
Bibliographic record
Abstract
The enthalpic interaction between 1-propanol (1P) molecules, , was evaluated in 1P-poly(ethylene glycol) (PEG)-H2O and 1P-tert-butyl alcohol (TBA)-H2O ternary mixtures. The model-free and experimentally accessible quantity, , indicates the effect of an additional 1P on the actual enthalpic situation of 1P in the mixture. It was shown earlier that the composition dependence of reflects the process how 1P modifies H2O. This pattern changes in the presence of a third component, PEG or TBA. The effects of PEG or TBA on the molecular organization of H2O were elucidated from these induced changes. Together with previous similar studies for the effects of methanol (ME), 2-propanol (2P), ethylene glycol (EG), and glycerol (Gly), we suggest a method and hence a possible scaling for sorting out hydrophobicity vs hydrophilicity of these alcohols by the changes induced to the loci of the maxima in . We show that hydrophilicity scales with the number of oxygen, regardless of whether O is the ether -O- or the hydroxyl -OH. Hydrophobicity also scales with the number of carbon atoms for alcohols without a methyl group. For those with methyl groups, the hydrophobicity seems proportional to the total number of carbon with a different proportionality factor from those without methyl group.
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 | 0.000 |
| 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".