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Record W4406362818 · doi:10.1021/acs.jpcb.4c06787

Structural and Dynamic Heterogeneity of Deep Eutectic Solvents Composed of Choline Chloride and Ortho-Phenol Derivatives

2025· article· en· W4406362818 on OpenAlexfundno aff
Derrick Poe, Dinis O. Abranches, Xiaoyu Wang, Jeffrey M. Klein, William Dean, Benworth Hansen, Brian Doherty, Carla C. Fraenza, Burcu Gurkan, Joshua Sangoro, Mark E. Tuckerman, Steve Greenbaum, Edward J. Maginn

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2025
Typearticle
Languageen
FieldChemical Engineering
TopicIonic liquids properties and applications
Canadian institutionsnot available
FundersAir Force Office of Scientific ResearchBasic Energy SciencesOffice of ScienceUniversity of Notre DameOak Ridge National LaboratoryYork UniversityU.S. Department of Energy
KeywordsCholine chlorideHydrogen bondEutectic systemChemistryMolecular dynamicsChlorideCatecholPhenolDeep eutectic solventThermal diffusivityComputational chemistryAb initioPhysical chemistryMoleculeThermodynamicsOrganic chemistry

Abstract

fetched live from OpenAlex

Structural, thermal, and dynamic properties of four deep eutectic solvents comprising choline chloride paired with ortho- phenolic derivative hydrogen-bond donors were probed using experiments and molecular simulations. The hydrogen-bond donors include phenol, catechol, o -chlorophenol, and o-cresol, in a 3:1 mixture with the hydrogen-bond acceptor choline chloride. Density, viscosity, and pulsed-field gradient NMR diffusivity measurements were conducted over a range of temperatures. Classical and ab initio molecular dynamics simulation results match experimental data reasonably well. The simulation results were then used to perform a more detailed analysis of the local structure and dynamics of these 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 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 score0.002
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 communication0.0000.000
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 head0.008
GPT teacher head0.256
Teacher spread0.248 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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