Sulfolane clustering in aqueous saline solutions
Bibliographic record
Abstract
Sulfolane is a groundwater pollutant. While sulfolane is miscible in pure water, its miscibility in the presence of ions has not been widely investigated. This aspect is relevant to predict sulfolane migration in groundwater. Na2SO4 separates sulfolane from water, yielding bulk separation, emulsions seen by optical microscopy or molecular sulfolane clusters. We study these clusters with fluorescence spectroscopy, small-angle x ray (SAXS) and neutron (SANS) scattering, and x-ray absorption fine structure (XAFS). Fluorescence spectroscopy reveals non-monotonic changes in the local viscosity of the water phase with 10% sulfolane and 0.25-1M Na2SO4, likely resulting from the interplay between sulfolane clustering and enhanced interactions between water molecules. NaCl affects the micro-viscosity of water similarly to Na2SO4, but at higher concentrations. At low sulfolane percentages, Cl− decreases the activity coefficients of water and sulfolane, suggesting stronger sulfolane–sulfolane and water–water interactions. SAXS confirms that Na2SO4 induces sulfolane clustering. SAXS data modeled with a power law and a Gaussian reveal a correlation length ξ = 16.3 Å, which we view as the radius of a sulfolane cluster surrounded by water and Na+ ions. SANS also shows that 2 mol. % of sulfate and chloride salts induce sulfolane clustering, with sulfates having a more marked effect. Furthermore, XAFS reveals that sulfates affect sulfolane sorption onto Si3N4 surfaces. Without Na2SO4, sulfolane directly sorbs onto Si3N4 surfaces. Conversely, with Na2SO4, water is directly sorbed onto Si3N4, likely because it surrounds sulfolane clusters. Also, hydrated Na+ ions are in Si3N4 surface proximity.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".