MétaCan
Menu
Back to cohort
Record W1990746302 · doi:10.1021/ic7020929

<i>N</i>-Heterocyclic Carbenes with Inorganic Backbones:  Electronic Structures and Ligand Properties

2008· article· en· W1990746302 on OpenAlexafffund
Anna Kausamo, Heikki M. Tuononen, K.E. Krahulic, Roland Roesler

Bibliographic record

VenueInorganic Chemistry · 2008
Typearticle
Languageen
FieldChemistry
TopicN-Heterocyclic Carbenes in Organic and Inorganic Chemistry
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaJyväskylän Yliopisto
KeywordsChemistryHeteroatomCarbeneLigand (biochemistry)Main group elementTransition metalMetalAcceptorComputational chemistryCrystallographyOrganic chemistryRing (chemistry)Catalysis

Abstract

fetched live from OpenAlex

The electronic structures of known N-heterocyclic carbenes (NHCs) with boron, nitrogen, and phosphorus backbones are examined using quantum chemical methods and compared to the experimental results and to the computational data obtained for a classical carbon analogue, imidazol-2-ylidene. The sigma-donor and pi-acceptor abilities of the studied NHCs in selected transition-metal complexes are evaluated using a variety of approaches such as energy and charge decomposition analysis, as well as calculated acidity constants and carbonyl stretching frequencies. The study shows that the introduction of selected heteroatoms into the NHC backbone generally leads to stronger metal-carbene bonds and therefore improves the ligand properties of these systems. The backdonation of pi-electron density from the metal to the ligand is found to be strong in complexes of the studied NHCs with group 11 metals, where it constitutes up to nearly 35% of the total orbital interaction energy. The ligand properties of the aluminum analogues of some of the reported NHCs with boron backbones are also assessed.

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), Insufficient payload (model declined to judge)
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 score0.007
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.009
GPT teacher head0.187
Teacher spread0.178 · 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

Citations66
Published2008
Admission routes2
Has abstractyes

Explore more

Same venueInorganic ChemistrySame topicN-Heterocyclic Carbenes in Organic and Inorganic ChemistryFrench-language works237,207