Density Functional Theory Prediction of the Electrocatalytic Mechanism of Proton Reduction by a Dicobalt Tetrakis(Schiff Base) Macrocycle

Tyler LeBlond, Peter H. Dinolfo

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

A dicobalt tetrakis(Schiff base) macrocycle has recently been reported to electrochemically catalyze the reduction of H+ to H2 in an acetonitrile solution. Density functional theory (DFT) calculations using the ωB97X-D functional are shown to produce structural and thermodynamic results in good agreement with the experimental data. A mechanistic model based on thermodynamics is developed that incorporates electrochemical and magnetic details of the complex, accounting for electron-spin reorganization of the metal center after redox steps. The model is validated through a comparison of the predicted electrochemical potentials with the irreversible cyclic voltammogram of [Co2LAc]+, which shows redox-coupled spin-crossover (RCSCO) behavior for the CoII/III transitions. Using our model, we predict the thermodynamically favored mechanism of H2 evolution by [Co2L]2+ to be one of heterolytic proton attack on a [CoII2L(μ-H)]+ species. Understanding the electronic details and thermodynamically preferred mechanism of this catalyst will aid in improving its efficiency and the future design of bimetallic Co-based H+ electrocatalysts. Also, this work will assist in the future DFT modeling of bimetallic RCSCO complexes.

Original languageEnglish
Pages (from-to)3764-3774
Number of pages11
JournalInorganic Chemistry
Volume59
Issue number6
DOIs
StatePublished - Mar 16 2020
Externally publishedYes

Funding

This work was supported in part by Rensselaer Polytechnic Institute and by the National Science Foundation under Contract CHE-1255100. This work used the Extreme Science and Engineering Discovery Environment (XSEDE; Grant TG-CHE130109), which is supported by National Science Foundation under Grant OCI-1053575.

FundersFunder number
National Science FoundationCHE-1255100, TG-CHE130109, OCI-1053575
Rensselaer Polytechnic Institute

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