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Record W4407173274 · doi:10.1016/j.colsuc.2025.100061

Sulfolane reduction by arginine and ferrous iron ions

2025· article· en· W4407173274 on OpenAlexafffund
Erica Pensini, Caitlyn Hsiung, Alejandro G. Marangoni, Joshua van der Zalm, Aicheng Chen, Nour Kashlan

Bibliographic record

VenueColloids and Surfaces C Environmental Aspects · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicChemical Synthesis and Characterization
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaShell Canada
KeywordsFerrousSulfolaneIonChemistryReduction (mathematics)ArginineInorganic chemistryBiochemistryMathematicsOrganic chemistry

Abstract

fetched live from OpenAlex

Sulfolane is a water-miscible, bioavailable, worldwide pollutant. While its aerobic biodegradation by bacteria is well documented, its abiotic degradation by amino acids and metal ions has never been reported. Here we find that Fe 2 + and arginine (ARG) reduce sulfolane to sulfoxide at circum-neutral pH, as shown by attenuated total reflection-Fourier transform infrared spectroscopy. Sulfolane reduction occurs at the surface of iron-ARG solid flocs, onto which sulfoxide remains sorbed even after rinsing with water volumes up to 16-fold the floc volume. Sulfolane reduction by Fe 2+ ions does not occur in the absence of ARG, which binds iron and affects its redox chemistry, as shown by cyclic voltammetry. Sulfolane reduction is also promoted by lysine, but not by histidine. Sulfolane is not reduced by Fe 3+ and ARG, indicating that this reaction requires Fe 2+ oxidation to Fe 3+ . The observed abiotic transformation of sulfolane may affect its fate in natural ecosystems.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.002
GPT teacher head0.172
Teacher spread0.170 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes2
Has abstractyes

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