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Record W2046404137 · doi:10.1139/v06-085

NMR study of hydrogen bonding in methanol carbon tetrachloride solutions

2006· article· en· W2046404137 on OpenAlexfundvenueno aff
Jia Deng, R. H. Lipson

Bibliographic record

VenueCanadian Journal of Chemistry · 2006
Typearticle
Languageen
FieldChemistry
TopicMolecular spectroscopy and chirality
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryMethanolCarbon tetrachlorideEnthalpyHydrogen bondChemical shiftTetramerMonomerHydrogenCluster (spacecraft)Physical chemistryTetrachlorideMoleculeThermodynamicsOrganic chemistryPolymer

Abstract

fetched live from OpenAlex

The nuclear magnetic resonance (NMR) frequencies of the hydroxyl protons of methanol clusters in carbon tetrachloride were measured as a function of concentration between 245.4 and 320.2 K. The size of the methanol cluster (n) and the enthalpy and entropy of methanol self-association were obtained from nonlinear least-squares fittings to a model that assumed that only one dominant cluster was in equilibrium with the monomer at each temperature. The chemical shift measurements at 273 and 299.1 K fit best to theoretical curves calculated for a monomer–tetramer equilibrium. However, at higher and lower temperatures, the analyses indicate that the most dominant clusters are smaller and larger, respectively.Key words: alcohol liquid solutions, structure, hydrogen bonding, thermodynamics of self-association, nuclear magnetic resonance.

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 score0.082
Threshold uncertainty score0.996

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 head0.011
GPT teacher head0.229
Teacher spread0.218 · 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
Published2006
Admission routes2
Has abstractyes

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