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Record W4312082859 · doi:10.1016/j.ssnmr.2022.101850

Fast magic angle spinning for the characterization of milligram quantities of organic and biological solids at natural isotopic abundance by 13C–13C correlation DNP-enhanced NMR

2022· article· en· W4312082859 on OpenAlexaff
Adam N. Smith, Rania Harrabi, Thomas Halbritter, Daniel Lee, Fabien Aussenac, Patrick C.A. van der Wel, Sabine Hediger, Snorri Th. Sigurdsson, Gaël De Paëpe

Bibliographic record

VenueSolid State Nuclear Magnetic Resonance · 2022
Typearticle
Languageen
FieldChemistry
TopicAdvanced NMR Techniques and Applications
Canadian institutionsBruker (Canada)
FundersNational Institutes of HealthAgence Nationale de la RechercheNational Institute of General Medical SciencesHáskóli ÍslandsDeutsche ForschungsgemeinschaftEuropean CommissionLABoratoires d’EXcellence ARCANE
KeywordsMicrocrystallineMagic angle spinningNatural abundanceChemistryMagic angleSolid-state nuclear magnetic resonanceCarbon-13 NMRCharacterization (materials science)MoleculeAnalytical Chemistry (journal)Nuclear magnetic resonance spectroscopyNuclear magnetic resonanceChemical physicsMaterials scienceCrystallographyOrganic chemistryNanotechnologyPhysicsChromatographyMass spectrometry

Abstract

fetched live from OpenAlex

C correlation spectra of biomolecular assemblies and microcrystalline organic molecules can be acquired at natural isotopic abundance with only milligram quantities of sample. These experiments combine fast Magic Angle Spinning of the sample, low-power dipolar recoupling, and dynamic nuclear polarization performed with AsymPol biradicals, a recently introduced family of polarizing agents. Such experiments are essential for structural characterization as they provide short- and long-range distance information. This approach is demonstrated on diverse sample types, including polyglutamine fibrils implicated in Huntington's disease and microcrystalline ampicillin, a small antibiotic molecule.

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)0.001
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: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.243
Teacher spread0.233 · 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

Citations11
Published2022
Admission routes1
Has abstractno

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