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Record W2030722624 · doi:10.1039/b907737e

Synthesis and solid-state characterization of platinum complexes with hexadentate amino- and iminophosphine ligands

2009· article· en· W2030722624 on OpenAlexaff
Marie‐Hélène Thibault, Bryan E. G. Lucier, Robert W. Schurko, Frédéric‐Georges Fontaine

Bibliographic record

VenueDalton Transactions · 2009
Typearticle
Languageen
FieldChemistry
TopicAdvanced NMR Techniques and Applications
Canadian institutionsUniversity of WindsorUniversité Laval
Fundersnot available
KeywordsChemistryCrystallographyPlatinumNuclear magnetic resonance spectroscopyPhosphineStereochemistrySpectroscopySolid-state nuclear magnetic resonanceImineNuclear magnetic resonanceOrganic chemistry

Abstract

fetched live from OpenAlex

Hexadentate ligands cis,cis-C(6)H(9)(N[double bond, length as m-dash]CHC(6)H(4)(PPh(2)))(3) () and cis,cis-C(6)H(9)(NHCH(2)C(6)H(4)(PPh(2)))(3) () were synthesized starting from cis,cis-1,3,5-triaminocyclohexane, and characterized using NMR spectroscopy and single-crystal X-ray diffraction. These ligands can bind both Pt(0) and Pt(II) metal centers using either or both of the soft phosphine moieties and the hard amine/imine moieties. In many cases the resulting complexes are negligibly soluble; hence, (31)P and (195)Pt solid-state NMR (SSNMR) spectroscopy was applied to analyse the bonding modes of the hexadentate ligands. The (195)Pt SSNMR spectroscopy of these complexes is particularly challenging, since (1)H-(195)Pt cross polarization is extremely inefficient, the (195)Pt longitudinal relaxation times are extremely long and the (195)Pt powder patterns are expected to be quite broad due to platinum chemical shift anisotropy. It is demonstrated that the ultra-wideline (195)Pt SSNMR spectra can be efficiently acquired with a combination of frequency-stepped piecewise acquisitions and cross-polarization/Carr-Purcell Meiboom-Gill (CP/CPMG) NMR experiments. The (195)Pt and (31)P SSNMR data are correlated to important structural features in both Pt(0) and Pt(II) species.

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 score0.082
Threshold uncertainty score0.445

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 head0.010
GPT teacher head0.252
Teacher spread0.242 · 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

Citations27
Published2009
Admission routes1
Has abstractyes

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