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Record W2789810384 · doi:10.1002/cphc.201701303

Characterizing the Effects of a “Switchable Water” Additive on the Aqueous Solubility of Small Molecules

2018· article· en· W2789810384 on OpenAlexafffund
Jesse R. Vanderveen, Sarika Burra, Jialing Geng, Alexandre Goyon, Alexander Pompermayer Jardine, Hyejin E. Shin, Tamer Andrea, Paul J. Dyson, Philip G. Jessop

Bibliographic record

VenueChemPhysChem · 2018
Typearticle
Languageen
FieldChemical Engineering
TopicIonic liquids properties and applications
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsChemistryAqueous solutionSolubilitySolvationSalt (chemistry)Inorganic chemistryMoleculeAmine gas treatingIonic bondingOrganic chemistryChemical engineeringIon

Abstract

fetched live from OpenAlex

Abstract “Switchable water” is an aqueous solution containing a water‐soluble amine additive that exhibits CO 2 ‐switchable properties, such as large changes in ionic strength, by forming an ammonium bicarbonate salt. Switchable water has been used to reversibly “salt‐out” organic compounds from water. This study explores the salting out of several compounds in switchable water when CO 2 is present and also explores the solubility of small molecules in switchable water, compared to pure water, when CO 2 is absent. The results show that organic compounds are generally more soluble in switchable water than pure water in the absence of CO 2 , but less soluble in the presence of 1 atm CO 2 . Exceptions include carboxylic acids and phenols which, presumably due to their acidity, are more soluble in switchable water than in pure water, even when CO 2 is applied. Kirkwood–Buff solvation theory was applied to gain insights into the effects of the amine additive on the aqueous solubility of caffeine. Furthermore, the switchable properties of the additives allow for the preparation of switchable aqueous two‐phase systems.

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 score0.004
Threshold uncertainty score0.234

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 head0.012
GPT teacher head0.205
Teacher spread0.193 · 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

Citations9
Published2018
Admission routes2
Has abstractyes

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