Densities for Sulfur in Benzene and Densities with Solubilities for a Eutectic Mixture of Biphenyl plus Diphenyl Ether: A General Solubility Equation for the Treatment of Aromatic Physical Sulfur Solvents
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Physical sulfur solvents are utilized in (i) the mitigation of sulfur deposition in sour gas wells and gathering lines and (ii) as a process medium for small-scale sulfur recovery. Non-aqueous aromatic solvents have two distinct advantages, where they are less reactive with elemental sulfur and provide for relatively large sulfur solubilities. Understanding the solubilities and physical properties within aromatic sulfur solvents over a wide range of temperature and pressure is important for both the previous applications. In this study, high-pressure densities for sulfur in benzene and a eutectic mixture of biphenyl and diphenyl ether were measured for T = (298.15–373.15) K and p < 100 MPa, where the densities were used to calculate the molar volume change upon dissolution. New sulfur solubility measurements are reported for sulfur in the eutectic mixture of biphenyl and diphenyl from T = (298.15–403.15) K and compared to benzene, toluene, and xylenes (BTX). The calculated volumetric changes and measured solubilities at atmospheric pressure were used to determine the enthalpy of dissolution, Δ X→sl H °, and the change in heat capacity, Δ X→sl C p °, for the α-sulfur, β-sulfur, and the liquid-sulfur phase dissolution using a van’t Hoff model. The eutectic solvent showed a higher solubility when compared to BTX at higher temperatures (β-sulfur and the liquid-sulfur regions) and both solubilities calculated to have a very small pressure dependence.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".