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Record W2009172464 · doi:10.2478/s11532-008-0034-3

Nuclear magnetic resonance spectroscopic studies of the trihexyl (tetradecyl) phosphonium chloride ionic liquid mixtures with water

2008· article· en· W2009172464 on OpenAlexafffund
Jerrod Dwan, Dan Durant, Khashayar Ghandi

Bibliographic record

VenueOpen Chemistry · 2008
Typearticle
Languageen
FieldChemical Engineering
TopicIonic liquids properties and applications
Canadian institutionsMount Allison University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhosphoniumIonic liquidChemistryChlorideInorganic chemistryIonic bondingChemical shiftMoleculeIonAlkylPhosphonium saltSalt (chemistry)Physical chemistryOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

Abstract Tetra-alkyl Phosphonium ionic liquids are phosphonium salts with melting points near room temperature. We report the NMR studies of water-trihexyl (tetradecyl) phosphonium chloride ionic liquid mixtures. The proton chemical shifts were used to investigate the intermolecular interactions in mixtures of ionic liquids and water. The OH chemical shifts were found to decrease as the water concentration in the ionic liquid increased, and their rate of change with temperature decreased with water concentration. The CH2 and CH3 chemical shifts were found to move downfield with the increase in temperature, and moved further downfield as water concentration was decreased. The interface of experimental data and the results of quantum calculations suggest a significant binding of phosphonium cations to chloride anion and water molecules. As well, the analysis of the data suggests a possible transformation from cationchloride-water configuration at low water concentrations to cation-water-water at higher water concentrations.

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)0.001
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.018
GPT teacher head0.233
Teacher spread0.215 · 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

Citations29
Published2008
Admission routes2
Has abstractyes

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