Impact of Organophosphorus Compounds on Sulfur Solubility in Organic and Amine Solvents for Sour Gas Reservoirs
Bibliographic record
Abstract
Abstract This study investigates the solubility of elemental sulfur in various solvents, including toluene, amines, and organophosphorus compounds, specifically triphenylphosphine (TPP) and tributylphosphine (TBP). The solubility of sulfur was tested under atmospheric pressure and ambient temperature, and the results showed that quinoline exhibited the highest sulfur solubility among the amines, while TPP and TBP significantly enhanced sulfur solubility in comparison to conventional solvents like toluene. A novel approach was introduced by eliminating the filtering and washing steps typically required for sulfur removal, using amine agents to prevent the precipitation of sulfur compounds. This modification made the process more efficient, faster, and suitable for practical field applications. Additionally, the use of TBP in sulfur solubility is a new concept with potential implications for sulfur management in the oil and gas industry. In this work, the formation of organophosphorus sulfide was also examined using gas chromatography. These findings provide a foundation for future research aimed at optimizing sulfur removal techniques for industrial applications.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".