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Record W2319447645 · doi:10.1021/ja0645180

Solid-State <sup>139</sup>La and <sup>15</sup>N NMR Spectroscopy of Lanthanum-Containing Metallocenes

2006· article· en· W2319447645 on OpenAlexaff
Hiyam Hamaed, Andy Y. H. Lo, David S. Lee, William J. Evans, Robert W. Schurko

Bibliographic record

VenueJournal of the American Chemical Society · 2006
Typearticle
Languageen
FieldChemistry
TopicAdvanced NMR Techniques and Applications
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsChemistryNuclear magnetic resonance spectroscopyLanthanumNMR spectra databaseTensor (intrinsic definition)Carbon-13 NMR satelliteElectric field gradientCrystallographySolid-state nuclear magnetic resonanceSpectroscopySpectral lineFluorine-19 NMRNuclear magnetic resonanceStereochemistryInorganic chemistryElectric fieldPhysics

Abstract

fetched live from OpenAlex

A preliminary set of solid-state 139La and 15N NMR data for lanthanum-containing metallocenes is presented, including (C5H5)3La, (C5Me4H)3La, [(C5Me5)2La]+[BPh4]-, and 15N-enriched [(C5Me4H)2La(THF)]215N2. Broad 139La NMR spectra, with breadths ranging from 600 kHz to 2.5 MHz, were acquired with piecewise QCPMG techniques at 9.4 T. Simulations of the spectra reveal 139La quadrupolar coupling constants (CQ) between 44 and 105 MHz. In addition, the first NMR measurement of a nitrogen chemical shift (CS) tensor for dinitrogen bound side-on to a metal atom is reported for [(C5Me4H)2La(THF)]215N2. The 139La NMR parameters show remarkable sensitivity to changes in metallocene structure and can be interpreted in an intuitive manner. Preliminary RHF and DFT calculations of 139La electric field gradient (EFG) and nitrogen CS tensors are used to provide tensor orientations and to rationalize the origin of the NMR parameters in terms of molecular structure and symmetry. The sensitivity of 139La and 15N NMR tensor parameters to changes in structure and bonding should prove invaluable in future studies of noncrystalline and disordered systems.

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 score0.022
Threshold uncertainty score0.699

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.007
GPT teacher head0.284
Teacher spread0.277 · 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

Citations42
Published2006
Admission routes1
Has abstractyes

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Same venueJournal of the American Chemical SocietySame topicAdvanced NMR Techniques and ApplicationsFrench-language works237,207