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Record W2016179283 · doi:10.1039/c3cp44016h

Theoretical and experimental insights into applicability of solid-state 93Nb NMR in catalysis

2013· article· en· W2016179283 on OpenAlexaff
Evgeniy S. Papulovskiy, Victor V. Terskikh, Chris J. Pickard, Olga B. Lapina

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2013
Typearticle
Languageen
FieldChemistry
TopicAdvanced NMR Techniques and Applications
Canadian institutionsNational Research Council Canada
FundersEngineering and Physical Sciences Research Council
KeywordsCASTEPNiobiumNMR spectra databaseNiobium oxideCarbon-13 NMR satelliteAb initioChemistryCarbon-13 NMRFluorine-19 NMRSolid-state nuclear magnetic resonancePhysical chemistryComputational chemistryNuclear magnetic resonance spectroscopyCatalysisProton NMRMaterials scienceDensity functional theorySpectral lineNuclear magnetic resonanceOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

Ab initio DFT calculations of (93)Nb NMR parameters using the NMR-CASTEP code were performed for a series of over fifty individual niobates, and a good agreement has been found with experimental NMR parameters. New experimental and calculated (93)Nb NMR data were obtained for several compounds, AlNbO4, VNb9O25, K8Nb6O19 and Cs3NbO8, which are of particular interest for catalysis. Several interesting trends have been identified between (93)Nb NMR parameters and the specifics of niobium site environments in niobates. These trends may serve as useful guidelines in interpreting (93)Nb NMR spectra of complex niobium oxide systems, including amorphous samples and niobium-based multicomponent heterogeneous catalysts. Potential applications of (93)Nb NMR to study solid polyoxoniobates are discussed.

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 categoriesMeta-epidemiology (narrow)
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 score0.195
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.273
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.

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

Citations55
Published2013
Admission routes1
Has abstractyes

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