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Record W2482520993 · doi:10.1021/acs.jpcb.6b05755

<sup>17</sup>O NMR Investigation of Water Structure and Dynamics

2016· article· en· W2482520993 on OpenAlexafffund
Eric G. Keeler, Vladimir K. Michaelis, Robert G. Griffin

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2016
Typearticle
Languageen
FieldChemistry
TopicAdvanced NMR Techniques and Applications
Canadian institutionsUniversity of Alberta
FundersNational Institute of Biomedical Imaging and BioengineeringNational Institute on AgingNational Institutes of HealthNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsChemistryMoleculeNuclear magnetic resonance spectroscopyMagnetic dipole–dipole interactionDipoleCoupling constantNuclear magnetic resonanceProton NMROxygen-17NMR spectra databaseSpectral lineJ-couplingResonance (particle physics)Molecular physicsAtomic physicsPhysicsStereochemistry

Abstract

fetched live from OpenAlex

The structure and dynamics of the bound water in barium chlorate monohydrate were studied with (17)O nuclear magnetic resonance (NMR) spectroscopy in samples that are stationary and spinning at the magic-angle in magnetic fields ranging from 14.1 to 21.1 T. (17)O NMR parameters of the water were determined, and the effects of torsional oscillations of the water molecule on the (17)O quadrupolar coupling constant (CQ) were delineated with variable temperature MAS NMR. With decreasing temperature and reduction of the librational motion, we observe an increase in the experimentally measured CQ explaining the discrepancy between experiments and predictions from density functional theory. In addition, at low temperatures and in the absence of (1)H decoupling, we observe a well-resolved (1)H-(17)O dipole splitting in the spectra, which provides information on the structure of the H2O molecule. The splitting arises because of the homogeneous nature of the coupling between the two (1)H-(17)O dipoles and the (1)H-(1)H dipole.

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 score0.028
Threshold uncertainty score0.184

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 head0.006
GPT teacher head0.237
Teacher spread0.231 · 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

Citations39
Published2016
Admission routes2
Has abstractyes

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