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Record W4301390522 · doi:10.1021/acs.jpcb.2c03985

Biradical Polarizing Agents at High Fields

2022· article· en· W4301390522 on OpenAlexafffund
Vladimir K. Michaelis, Eric G. Keeler, Salima Bahri, Ta‐Chung Ong, Eugenio Daviso, Michael T. Colvin, Robert G. Griffin

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2022
Typearticle
Languageen
FieldChemistry
TopicAdvanced NMR Techniques and Applications
Canadian institutionsUniversity of Alberta
FundersNational Institute on AgingNational Institute of General Medical SciencesNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsMagnetic fieldLiquid nitrogenNuclear magnetic resonance spectroscopyPolarization (electrochemistry)Nuclear magnetic resonanceSpectroscopyMicrowaveMaterials scienceField (mathematics)Chemical physicsAnalytical Chemistry (journal)ChemistryComputer sciencePhysicsPhysical chemistryTelecommunicationsOrganic chemistry

Abstract

fetched live from OpenAlex

The sensitivity enhancements available from dynamic nuclear polarization (DNP) are rapidly reshaping the research landscape and expanding the field of nuclear magnetic resonance (NMR) spectroscopy as a tool for solving complex chemical and structural problems. The past decade has seen considerable advances in this burgeoning method, while efforts to further improve its capabilities continue along many avenues. In this report, we examine the influence of static magnetic field strength and temperature on the reported 1 H DNP enhancements from three conventional organic biradicals: TOTAPOL, AMUPol, and SPIROPOL. In contrast to the conventional wisdom, our findings show that at liquid nitrogen temperatures and 700 MHz/460.5 GHz, these three bisnitroxides all provide similar 1 H DNP enhancements, ε ≈ 60. Furthermore, we investigate the influence of temperature, microwave power, magnetic field strength, and protein sample deuteration on the NMR experimental results.

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 score0.006

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)0.0020.001

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 head0.014
GPT teacher head0.276
Teacher spread0.262 · 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

Citations5
Published2022
Admission routes2
Has abstractyes

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Same venueThe Journal of Physical Chemistry BSame topicAdvanced NMR Techniques and ApplicationsFrench-language works237,207