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Record W2770944560 · doi:10.1002/cmr.a.21409

Probing nitrite ion dynamics in Na<scp>NO</scp><sub>2</sub> crystals by solid‐state <sup>17</sup>O <scp>NMR</scp>

2016· article· en· W2770944560 on OpenAlexafffund
Yizhe Dai, Ivan Hung, Zhehong Gan, Gang Wu

Bibliographic record

VenueConcepts in Magnetic Resonance Part A · 2016
Typearticle
Languageen
FieldChemistry
TopicAdvanced NMR Techniques and Applications
Canadian institutionsQueen's University
FundersDivision of Materials ResearchNatural Sciences and Engineering Research Council of Canada
KeywordsChemistrySolid-state nuclear magnetic resonanceNMR spectra databaseCrystallographyIonAnalytical Chemistry (journal)Carbon-13 NMR satelliteNuclear magnetic resonance spectroscopyJ-couplingSpectral linePhysical chemistryNuclear magnetic resonanceStereochemistryFluorine-19 NMRPhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract We report a solid‐state 17O (I = 5/2) NMR study of the nitrite ion dynamics in crystalline NaNO2. Variable temperature (VT) 17O NMR spectra were recorded at 3 magnetic fields, 11.7, 14.1, and 21.1 T. The VT 17O NMR data suggest that the ion in the ferroelectric phase of NaNO2 undergoes 2‐fold flip motion about the crystallographic b axis and the corresponding rotational barrier is 68 ± 5 kJ mol−1. We also obtained a 2D 17O EXSY spectrum for a stationary sample of NaNO2 at 250 K, which, in combination with 1D 17O NMR spectral analyses, allowed precise determination of the relative orientation between the 17O quadrupolar coupling and chemical shift tensors in the molecular frame of reference. The experimentally determined 17O NMR tensors for NaNO2 were in agreement with quantum chemical calculations produced by a periodic DFT code BAND.

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 score0.004

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)0.0010.000

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 head0.009
GPT teacher head0.258
Teacher spread0.250 · 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

Citations9
Published2016
Admission routes2
Has abstractyes

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Same venueConcepts in Magnetic Resonance Part ASame topicAdvanced NMR Techniques and ApplicationsFrench-language works237,207