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Record W2441656811 · doi:10.1021/acs.jpcb.5b04647

Structural Insights into Bound Water in Crystalline Amino Acids: Experimental and Theoretical <sup>17</sup>O NMR

2015· article· en· W2441656811 on OpenAlexafffund
Vladimir K. Michaelis, Eric G. Keeler, Ta‐Chung Ong, Kimberley N. Craigen, Susanne Penzel, John E. C. Wren, Scott Kroeker, Robert G. Griffin

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2015
Typearticle
Languageen
FieldChemistry
TopicAdvanced NMR Techniques and Applications
Canadian institutionsUniversity of Manitoba
FundersNational Institute of Biomedical Imaging and BioengineeringNational Institutes of HealthNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsMagic angle spinningMoleculeBound waterChemistryChemical shiftNeutron diffractionCrystallographyNuclear magnetic resonance spectroscopyCrystal structurePhysical chemistryStereochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

We demonstrate here that the (17)O NMR properties of bound water in a series of amino acids and dipeptides can be determined with a combination of nonspinning and magic-angle spinning experiments using a range of magnetic field strengths from 9.4 to 21.1 T. Furthermore, we propose a (17)O chemical shift fingerprint region for bound water molecules in biological solids that is well outside the previously determined ranges for carbonyl, carboxylic, and hydroxyl oxygens, thereby offering the ability to resolve multiple (17)O environments using rapid one-dimensional NMR techniques. Finally, we compare our experimental data against quantum chemical calculations using GIPAW and hybrid-DFT, finding intriguing discrepancies between the electric field gradients calculated from structures determined by X-ray and neutron diffraction.

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 score0.067
Threshold uncertainty score0.423

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 head0.011
GPT teacher head0.279
Teacher spread0.268 · 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

Citations46
Published2015
Admission routes2
Has abstractyes

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