The rheology of liquid elemental sulfur across the λ-transition
Bibliographic record
Abstract
Seventy million tons of sulfur were produced worldwide in 2016. Much of the transportation and handling of molten sulfur require sulfur pumps which are challenging to operate due to the anomalous behavior of sulfur's liquid viscosity at temperatures near its λ-transition region. Sulfur's viscosity decreases from ca. 30 × 10−2 to ca. 7 × 10−2 Pa s when heating from the melt at T = 115 °C and then increases dramatically at T > 160 °C to a maximum of 93 000 × 10−2 Pa s at T = 187 °C. While the temperature of this viscosity transition is well known, no shear related information can be found in the literature and previous low-shear data do not address thermal hysteresis behavior (viscosity differences for cooling or heating) when sulfur is heated beyond 210 °C. This work is focused on reinvestigating the low-shear viscosity of molten sulfur which was initially studied by Bacon and Fanelli [J. Am. Chem. Soc. 65, 639–648 (1943)] and the effects of high-shear. The rheology of molten sulfur was studied using an Anton-Paar MCR 302 rheometer equipped with a 1000 bars pressure cell. Viscosity profiles, including attenuation during heating or cooling are discussed, as well as the rheological behavior of molten sulfur encountered in pipe flow and pumping shear regions. Data were used to fit a semiempirical equation based on a previous reptation model. In addition, critical shear rates were measured and reported, where liquid sulfur becomes non-Newtonian at high-shear and in regions where polymerization is significant.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".