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Record W2313524385 · doi:10.1021/es5035602

Measuring and Modeling the Salting-out Effect in Ammonium Sulfate Solutions

2014· article· en· W2313524385 on OpenAlexafffund
Chen Wang, Ying Duan Lei, Satoshi Endo, Frank Wania

Bibliographic record

VenueEnvironmental Science & Technology · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAmmonium sulfateSalting outSulfateChemistryAmmoniumSaltingEnvironmental chemistryEnvironmental scienceChromatographyOrganic chemistryAqueous solutionFood science

Abstract

fetched live from OpenAlex

The presence of inorganic salts significantly influences the partitioning behavior of organic compounds between environmentally relevant aqueous phases, such as seawater or aqueous aerosol, and other, nonaqueous phases (gas phase, organic phase, etc.). In this study, salting-out coefficients (or Setschenow constants) (KS [M(-1)]) for 38 diverse neutral compounds in ammonium sulfate ((NH4)2SO4) solutions were measured using a shared headspace passive dosing method and a negligible depletion solid phase microextraction technique. The measured KS were all positive, varied from 0.216 to 0.729, and had standard errors in the range of 0.006-0.060. Compared to KS for sodium chloride (NaCl) in the literature, KS values for (NH4)2SO4 are always higher for the same compound, suggesting a higher salting-out effect of (NH4)2SO4. A polyparameter linear free energy relationship (pp-LFER) for predicting KS in (NH4)2SO4 solutions was generated using the experimental data for calibration. pp-LFER predicted KS agreed well with measured KS reported in the literature. KS for (NH4)2SO4 was also predicted using the quantum-chemical COSMOtherm software and the thermodynamic model AIOMFAC. While COSMOtherm generally overpredicted the experimental KS, predicted and experimental values were correlated. Therefore, a fitting factor needs to be applied when using the current version of COSMOtherm to predict KS. AIOMFAC tends to underpredict the measured KS((NH4)2SO4) but always overpredicts KS(NaCl). The prediction error is generally larger for KS(NaCl) than for KS((NH4)2SO4). AIOMFAC also predicted a dependence of KS on the salt concentrations, which is not observed in the experimental data. In order to demonstrate that the models developed and calibrated in this study can be applied to estimate Setschenow coefficients for atmospherically relevant compounds involved in secondary organic aerosol formation based on chemical structure alone, we predicted and compared KS for selected α-pinene oxidation products.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.014
GPT teacher head0.188
Teacher spread0.174 · 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 designSimulation or modeling
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

Citations76
Published2014
Admission routes2
Has abstractyes

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