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Record W2768029964 · doi:10.1002/cphc.201700990

Structural Insights from <sup>59</sup>Co Solid‐State NMR Experiments on Organocobalt(I) Catalysts

2017· article· en· W2768029964 on OpenAlexafffund
Kevin M. N. Burgess, Cory M. Widdifield, Yang Xu, César Leroy, David L. Bryce

Bibliographic record

VenueChemPhysChem · 2017
Typearticle
Languageen
FieldChemistry
TopicAdvanced NMR Techniques and Applications
Canadian institutionsUniversity of OttawaWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectric field gradientChemistrySolid-state nuclear magnetic resonanceNuclear magnetic resonance spectroscopyChemical shiftCobaltDensity functional theoryCatalysisCrystallographySpectroscopyCarbon-13 NMRPhysical chemistryComputational chemistryNuclear magnetic resonanceStereochemistryInorganic chemistryQuadrupoleOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

Abstract A series of fumarate‐based organocobalt(I) [CoCp(CO)(fumarate)] catalysts is synthesized and characterized by X‐ray crystallography, multinuclear ( 13 C and 59 Co) solid‐state NMR spectroscopy, and 59 Co NQR spectroscopy. Given the dearth of 59 Co solid‐state NMR studies on Co I compounds, the present work constitutes the first systematic characterization of the 59 Co electric field gradient and chemical shift tensors for a series of cobalt complexes in this oxidation state. Using X‐ray crystallography, the molecular geometry about the Co I centre is found to be nearly identical in all compounds studied herein. Owing to the 59 Co nucleus’ large chemical shift range, solid‐state NMR experiments are found to be able to detect small structural differences between the individual organocobalt(I) compounds. With the aid of density functional theory calculations on these complexes, it is shown that the 59 Co chemical shift anisotropy and the 59 Co quadrupolar coupling constant are both extremely sensitive gauges of the Fu‐Co‐Cp bond angle, providing a link between these 59 Co NMR observables and the catalysts’ structures.

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 score0.030
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.023
GPT teacher head0.318
Teacher spread0.295 · 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

Citations9
Published2017
Admission routes2
Has abstractyes

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