MétaCan
Menu
Back to cohort
Record W2047454965 · doi:10.1002/cphc.200900624

Probing the Local Structure of Pure Ionic Liquid Salts with Solid‐ and Liquid‐State NMR

2009· article· en· W2047454965 on OpenAlexaff
Peter G. Gordon, Darren H. Brouwer, John A. Ripmeester

Bibliographic record

VenueChemPhysChem · 2009
Typearticle
Languageen
FieldChemical Engineering
TopicIonic liquids properties and applications
Canadian institutionsCarleton UniversitySteacie Institute for Molecular Sciences
Fundersnot available
KeywordsIonic liquidSolid-state nuclear magnetic resonanceChemistrySolid-stateLocal structureIonic bondingNuclear magnetic resonance spectroscopyLiquid statePhysical chemistryMaterials scienceCrystallographyOrganic chemistryIonNuclear magnetic resonancePhysics

Abstract

fetched live from OpenAlex

Room-temperature ionic liquids (RTILs) are gaining increasing interest and are considered part of the green chemistry paradigm due to their negligible vapour pressure and ease of recycling. Evidence of liquid-state order, observed by IR and Raman spectroscopy, diffraction studies, and simulated by ab initio methods, has been reported in the literature. Here, quadrupolar nuclei are used as NMR probes to extract information about the solid and possible residual order in the liquid state of RTILs. To this end, the anisotropic nature and field dependence of quadrupolar and chemical shift interactions are exploited. Relaxation time measurements and a search for residual second-order quadrupolar coupling were employed to investigate the molecular motions present in the liquid state and infer what kind of order is present. The results obtained indicate that on a timescale of approximately 10(-8) sec or longer, RTILs behave as isotropic liquids without residual order.

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 score0.004

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.007
GPT teacher head0.210
Teacher spread0.204 · 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

Citations34
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueChemPhysChemSame topicIonic liquids properties and applicationsFrench-language works237,207