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Record W4406445804 · doi:10.1016/j.molliq.2025.126940

Sulfolane facilitates diisopropylamine dissolution in water, potentially enhancing pollutant transport

2025· article· en· W4406445804 on OpenAlexafffund
Erica Pensini, Alejandro G. Marangoni, Sylvain Prévost

Bibliographic record

VenueJournal of Molecular Liquids · 2025
Typearticle
Languageen
FieldChemistry
TopicRadioactive element chemistry and processing
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaShell Canada
KeywordsSulfolaneDissolutionPollutantEnvironmental chemistryChemistryEnvironmental scienceOrganic chemistry

Abstract

fetched live from OpenAlex

• Sulfolane and diisopropylamine are found as co-pollutants in groundwater. • Sulfolane promotes diisopropylamine miscibility in water. • Sulfolane hampers diisopropylamine sorption onto minerals. • Sulfolane can promote diisopropylamine migration across aquifers. Diisopropylamine (DIPA) and sulfolane are emerging groundwater co-contaminants, used in different industrial processes including carbon capture. In binary aqueous mixtures, sulfolane is freely miscible in water, with limited sorption onto minerals. Instead, DIPA yields sub-micron dispersions in water and sorbs onto different minerals. This limits DIPA migration in groundwater. Ternary aqueous mixtures of DIPA and sulfolane have not been studied before. Here we show that sulfolane enhances DIPA miscibility in water, although sulfolane and DIPA are immiscible without water. This unexpected co-solvency effect inhibits DIPA sorption onto minerals. Small angle neutron scattering experiments reveal composite droplets with a small DIPA core (diameter ≈ 12 Å) surrounded by sulfolane, which disperses DIPA. Fourier transform infrared spectroscopy and computer simulations show interactions between DIPA and sulfolane in water, which in aid in solubilizing DIPA in water. These findings will aid in predicting and preventing co-pollutant migration in groundwater.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.588

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.234
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2025
Admission routes2
Has abstractyes

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