Pointyng Correction in Phase Equilibrium Thermodynamics of Non Polar Gases and Brines: A Review
Bibliographic record
Abstract
Abstract: The development of an equation for Poynting correction, is of paramount importance in the field of modeling and interpretation of phase equilibrium thermodynamics in a wide range of temperatures and pressures /densities for practical applications in chemistry, physical chemistry, geochemistry and industrial technology. The unique previous existing equation for Poynting correction is a simple expression, represented by a product of a constant volume by a pressure difference between wich the correction is applied. [1] proposed an equation based on a semitheoretical expression for the partial molar volume at infinite dilution of volatile aqueous non-electrolyte solute (ν20) that considers as variables the density and isothermal compressibility of the solvent, as well as the second cross solvent-solute virial coefficient (β12) and the second virial coefficient of pure solvent (β11). The mathematical equation is applicable to solutes whose β12 is known or can be estimated, in a temperature range of 273.16 K to 647 K, values of pressure up to 2 kbar and brines with ionic strength equal to 6 m NaCl. The expression provides excellent correlation with experimental data as shown for the H2S-H2O-NaCl system (with maximum deviation of 7%), through a thermodynamic model that uses this formula proposal coupled with the Law of Henry and the Soave- Redlich-Kwong equation of state for modeling the liquid-vapor phase equilibria.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".