Molecular Diffusivity of Phenol in Sub- and Supercritical Water: Application of the Split-Flow Taylor Dispersion Technique
Bibliographic record
Abstract
The binary diffusion coefficient of phenol in aqueous solution was examined from ambient to supercritical water conditions by using the developed split-flow Taylor dispersion technique. The technique significantly simplifies diffusivity measurements in high-temperature and supercritical water, as the sample injection and detection are performed ex situ at ambient conditions. The binary diffusion coefficient of phenol increases from 1.013 × 10(-9) m(2) s(-1) at 298.7 K and 25 MPa to about 34.71 × 10(-9) m(2) s(-1) at 672.9 K and 30 MPa and follows Arrhenius behavior with an activation energy of 15.09 kJ/mol. The diffusion coefficient of phenol in infinitely dilute solution was also calculated by means of molecular dynamics (MD) simulations over a wide temperature and density range (298-773 K and from 0.07 to 1.0 g/cm(3), respectively). A dramatic increase in the diffusivity was observed upon transition into the low density supercritical region. The obtained experimental data agrees well with available literature values and the MD results. At subcritical conditions the experimentally obtained binary diffusion coefficients generally follow the predictions from the Stokes-Einstein equation, with the estimate for the hydrodynamic radius of the solute taken from MD data.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".