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Record W4392504970 · doi:10.1016/j.ssnmr.2024.101923

Double-rotation (DOR) NMR spectroscopy: Progress and perspectives

2024· article· en· W4392504970 on OpenAlexafffund
David L. Bryce

Bibliographic record

VenueSolid State Nuclear Magnetic Resonance · 2024
Typearticle
Languageen
FieldChemistry
TopicAdvanced NMR Techniques and Applications
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNuclear magnetic resonance spectroscopySolid-state nuclear magnetic resonanceMagic angle spinningDecoupling (probability)SpinningSpectroscopyRotor (electric)ChemistryCarbon-13 NMR satelliteNuclear magnetic resonancePhysicsFluorine-19 NMREngineering

Abstract

fetched live from OpenAlex

Double-rotation (DOR) solid-state NMR spectroscopy is a high-resolution technique developed in the late 1980s. Although multiple-quantum magic-angle spinning (MQMAS) became the most widely used high-resolution method for half-integer spin quadrupoles after 1995, development and application of DOR NMR to a variety of chemical and materials science problems has endured. This Trend article recapitulates the development of DOR NMR, discusses various applications, and describes possible future directions. The main technical limitations specific to DOR NMR are simply related to the size of the double rotor system. The relatively large outer rotor (and thus coil) used for most applications over the past 35 years translates into relatively low rotor spinning frequencies, a low filling factor, and weak radiofrequency powers available for excitation and for proton decoupling. Ongoing developments in NMR instrumentation, including ever-shrinking MAS rotors and spherical NMR rotors, could solve many of these problems and may augur a renaissance for DOR NMR.

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)0.004
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0020.006
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.002

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 head0.013
GPT teacher head0.293
Teacher spread0.280 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations2
Published2024
Admission routes2
Has abstractyes

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