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Record W2920956934 · doi:10.1021/acs.jpcb.9b02277

High-Resolution <sup>17</sup>O NMR Spectroscopy of Structural Water

2019· article· en· W2920956934 on OpenAlexfundno aff
Eric G. Keeler, Vladimir K. Michaelis, C. Blake Wilson, Ivan Hung, Xiao-Ling Wang, Zhehong Gan, Robert G. Griffin

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2019
Typearticle
Languageen
FieldChemistry
TopicAdvanced NMR Techniques and Applications
Canadian institutionsnot available
FundersHigh Magnetic Field Laboratory, Chinese Academy of SciencesDivision of Materials ResearchNational Institute of Biomedical Imaging and BioengineeringNational Institute of General Medical SciencesNatural Sciences and Engineering Research Council of CanadaNational Science FoundationNational Institutes of HealthGovernment of CanadaNational Institute on AgingNational High Magnetic Field Laboratory
KeywordsNuclear magnetic resonance spectroscopySpectroscopyResolution (logic)High resolutionChemistryNuclear magnetic resonanceMaterials scienceCrystallographyAnalytical Chemistry (journal)PhysicsGeologyComputer scienceChromatographyRemote sensing

Abstract

fetched live from OpenAlex

The importance of studying site-specific interactions of structurally similar water molecules in complex systems is well known. We demonstrate the ability to resolve four distinct bound water environments within the crystal structure of lanthanum magnesium nitrate hydrate via 17O solid state nuclear magnetic resonance (NMR) spectroscopy. Using high-resolution multidimensional experiments at high magnetic fields (18.8–35.2 T), each individual water environment was resolved. The quadrupole coupling constants and asymmetry parameters of the 17O of each water were determined to be between 6.6 and 7.1 MHz, 0.83 and 0.90, respectively. The resolution of the four unique, yet similar, structural waters within a hydrated crystal via 17O NMR spectroscopy demonstrates the ability to decipher the unique electronic environment of structural water within a single hydrated crystal structure.

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 score0.015
Threshold uncertainty score0.883

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)0.0010.000

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 head0.006
GPT teacher head0.256
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 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

Citations37
Published2019
Admission routes1
Has abstractyes

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