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Record W2015441629 · doi:10.1039/c2gc16670d

Solvatochromic parameters for solvents of interest in green chemistry

2012· article· en· W2015441629 on OpenAlexafffund
Philip G. Jessop, David A. Jessop, Dongbao Fu, Lam Phan

Bibliographic record

VenueGreen Chemistry · 2012
Typearticle
Languageen
FieldChemical Engineering
TopicIonic liquids properties and applications
Canadian institutionsQueen's University
FundersUniversity of California, DavisUniversity of WaterlooKillam TrustsNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsSolvatochromismPolarizabilityIonic liquidPolarity (international relations)ChemistrySolvent polaritySolventEthylene glycolNile redIonic bondingAqueous solutionSolvent effectsOrganic chemistryComputational chemistryPhysical chemistryChemical physicsMoleculeCatalysisIonOptics

Abstract

fetched live from OpenAlex

Solvatochromic data have been collected from the literature or newly measured for 83 molecular solvents, 18 switchable solvents, and 187 ionic liquids that have been cited in the green chemistry literature. The data include the normalized Reichardt's parameter (ENT), the Nile red λmax, and the Kamlet–Taft parameters (α, β, and π*). Disagreements within the literature about the properties of glycerol and poly(ethylene glycol) have been resolved with new data. The switching of a switchable-polarity solvent (also known as a reversible ionic liquid) by CO2 causes a significant increase in polarity/polarizability (π*) but no change in the basicity (β). A switchable-hydrophilicity solvent undergoes an even greater change in polarity because it merges with an aqueous phase upon exposure to CO2. Trends observed from the data of ionic liquids are presented, along with concerns about the best method for determining the Kamlet–Taft parameters.

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)0.003
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 score0.013
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.008
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0130.003

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 head0.055
GPT teacher head0.259
Teacher spread0.203 · 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

Citations567
Published2012
Admission routes2
Has abstractyes

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