MétaCan
Menu
Back to cohort
Record W2040603228 · doi:10.1021/jp908206c

High Field <sup>33</sup>S Solid State NMR and First-Principles Calculations in Potassium Sulfates

2009· article· en· W2040603228 on OpenAlexafffund
Igor Moudrakovski, Stephen Lang, Serguei Patchkovskii, John A. Ripmeester

Bibliographic record

VenueThe Journal of Physical Chemistry A · 2009
Typearticle
Languageen
FieldChemistry
TopicAdvanced NMR Techniques and Applications
Canadian institutionsSteacie Institute for Molecular Sciences
FundersNational Research Council CanadaUniversity of OttawaOntario Innovation Trust
KeywordsChemistryCASTEPPseudopotentialAtomic orbitalGaussianBasis setGyromagnetic ratioDensity functional theoryWave functionHypervalent moleculeAb initio quantum chemistry methodsField (mathematics)Tensor (intrinsic definition)Molecular physicsAtomic physicsComputational chemistryMagnetic momentPhysical chemistryMoleculeQuantum mechanicsPhysicsElectron

Abstract

fetched live from OpenAlex

A set of potassium sulfates presenting a variety of sulfur environments (K(2)SO(4), KHSO(4), K(2)S(2)O(7), and K(2)S(2)O(8)) has been studied by (33)S solid state NMR at 21 T. Low natural abundance (0.75%) and small gyromagnetic ratio of (33)S presented a serious challenge even at such a high magnetic field. Nevertheless, using the QCPMG technique we were able to obtain good signals from the sites with C(Q) values approaching 16 MHz. Assignment of the sites and the relative orientations of the EFG tensors were assisted by quantum mechanical calculations using the Gaussian 98 and CASTEP packages. The Gaussian 98 calculations were performed using the density functional method and gauge independent atomic orbitals on molecular clusters of about 100-120 atoms. The CASTEP calculations utilized periodic boundary conditions and a gauge-including projector augmented-wave pseudopotential approach. Although only semiquantitative agreement is observed between the experimental and calculated parameters, the calculations are a very useful aid in the interpretation of experimental data.

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 score0.259
Threshold uncertainty score0.418

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 head0.274
Teacher spread0.263 · 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

Citations28
Published2009
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of Physical Chemistry ASame topicAdvanced NMR Techniques and ApplicationsFrench-language works237,207