The Importance of Ligand-Induced Backdonation in the Stabilization of Square Planar d10 Nickel π-Complexes.
Addison N DesnoyerWeiying HeShirin BehyanWeiling ChiuJennifer A LovePierre KennepohlPublished in: Chemistry (Weinheim an der Bergstrasse, Germany) (2019)
The electronic nature of Ni π-complexes is underexplored even though these complexes have been widely postulated as intermediates in organometallic chemistry. Herein, the geometric and electronic structure of a series of nickel π-complexes, Ni(dtbpe)(X) (dtbpe=1,2-bis(di-tert-butyl)phosphinoethane; X=alkene or carbonyl containing π-ligands), is probed using a combination of 31 P NMR, Ni K-edge XAS, Ni Kβ XES, and DFT calculations. These complexes are best described as square planar d10 complexes with π-backbonding acting as the dominant contributor to M-L bonding to the π-ligand. The degree of backbonding correlates with 2 JPP from NMR and the energy of the Ni 1s→4pz pre-edge in the Ni K-edge XAS data, and is determined by the energy of the π*ip ligand acceptor orbital. Thus, unactivated olefinic ligands tend to be poor π-acids whereas ketones, aldehydes, and esters allow for greater backbonding. However, backbonding is still significant even in cases in which metal contributions are minor. In such cases, backbonding is dominated by charge donation from the diphosphine, which allows for strong backdonation, although the metal centre retains a formal d10 electronic configuration. This ligand-induced backbonding can be formally described as a 3-centre-4-electron (3c-4e) interaction, in which the nickel centre mediates charge transfer from the phosphine σ-donors to the π*ip ligand acceptor orbital. The implications of this bonding motif are described with respect to both structure and reactivity.
Keyphrases
- metal organic framework
- magnetic resonance
- solar cells
- diabetic rats
- density functional theory
- drug induced
- oxidative stress
- carbon nanotubes
- gold nanoparticles
- molecular dynamics
- mass spectrometry
- pseudomonas aeruginosa
- staphylococcus aureus
- machine learning
- oxide nanoparticles
- crystal structure
- artificial intelligence
- monte carlo