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Record W4232267718 · doi:10.1002/ange.201504292

Efficient Dynamic Nuclear Polarization at 800 MHz/527 GHz with Trityl‐Nitroxide Biradicals

2015· article· en· W4232267718 on OpenAlexfundno aff
Guinevere Mathies, Marc A. Caporini, Vladimir K. Michaelis, Yangping Liu, Kan‐Nian Hu, Deni Mance, Jay L. Zweíer, Mélanie Rosay, Marc Baldus, Robert G. Griffin

Bibliographic record

VenueAngewandte Chemie · 2015
Typearticle
Languageen
FieldChemistry
TopicAdvanced NMR Techniques and Applications
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaGovernment of Canada
KeywordsNitroxide mediated radical polymerizationMagic angle spinningChemistryPolarization (electrochemistry)SolventMoleculePhotochemistryNuclear magnetic resonanceNuclear magnetic resonance spectroscopyPhysical chemistryOrganic chemistryPolymerPhysicsRadical polymerization

Abstract

fetched live from OpenAlex

Abstract Cross‐effect (CE) dynamic nuclear polarization (DNP) is a rapidly developing technique that enhances the signal intensities in magic‐angle spinning (MAS) NMR spectra. We report CE DNP experiments at 211, 600, and 800 MHz using a new series of biradical polarizing agents referred to as TEMTriPols, in which a nitroxide (TEMPO) and a trityl radical are chemically tethered. The TEMTriPol molecule with the optimal performance yields a record 1 H NMR signal enhancement of 65 at 800 MHz at a concentration of 10 m M in a glycerol/water solvent matrix. The CE DNP enhancement for the TEMTriPol biradicals does not decrease as the magnetic field is increased in the manner usually observed for bis‐nitroxides. Instead, the relatively strong exchange interaction between the trityl and nitroxide moieties determines the magnetic field at which the optimum enhancement is observed.

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 score0.107
Threshold uncertainty score0.739

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 head0.015
GPT teacher head0.259
Teacher spread0.244 · 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

Citations48
Published2015
Admission routes1
Has abstractyes

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