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Record W1974416805 · doi:10.1139/v06-172

Dynamic NMR spectroscopy of 2,2′-dimethyl-1-picrylhydrazine in various solvents

2006· article· en· W1974416805 on OpenAlexfundvenueno aff
Kathlynn C. Brown, M. F. El Bermani, Yamini Upadrashta, John A. Weil

Bibliographic record

VenueCanadian Journal of Chemistry · 2006
Typearticle
Languageen
FieldChemistry
TopicMolecular spectroscopy and chirality
Canadian institutionsnot available
FundersUniversity of Saskatchewan
KeywordsAtropisomerChemistrySolventNuclear magnetic resonance spectroscopyMoleculeSpectroscopySolvent effectsHydrazine (antidepressant)Computational chemistryChemical shiftCarbon-13 NMRPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

We have studied the 1H NMR spectra of 2,2′-dimethyl-1-(2,4,6-trinitrophenyl)hydrazine at 300 and 500 MHz in seven liquid solvents, with a view to learning details of the internal conformational changes taking place as a function of temperature and of the solvent. These molecules in liquid solution occur as interconverting enantiomorphic pairs (atropisomers). Advanced techniques for obtaining the correct activation energies and pseudo-thermodynamic parameters have been utilized, and these parameters are listed and discussed. Our results point to a transformation between the pair of atropisomers that is not quite as complicated as one might have encountered in that the solvent does not affect ΔG‡ in any major fashion. Molecular orbital calculations clarified some of the chemical shifts observed for both 1H and 13C. One goal of this study was to enable a detailed comparison with similar results available for 2,2′-diphenyl-1-(2,4,6-trinitrophenyl)hydrazine.Key words: dynamic NMR, dimethylpicrylhydrazine, hindered rotation, atropisomers, activation 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)0.000
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 score0.002
Threshold uncertainty score0.003

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.003
GPT teacher head0.216
Teacher spread0.213 · 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

Citations4
Published2006
Admission routes2
Has abstractyes

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