The “Fuel cells” have received global attention over the past few years due to the depletion of the world’s fossil fuel sources. The polyelectrolyte-based membrane is the key component of a fuel cell which acts as the ionic conductor between two electrodes. While anion exchange membranes (AEMs) in alkaline fuel cells are highly efficient and economical, traditional quaternary ammonium cations could suffer catastrophic degradation under highly alkaline conditions. To overcome this challenge, it is suggested to use membranes with alkaline stable metal cations over conventional organo-polyelectrolytes. Cobaltocenium derivatives are considered for use in AEMs because of their excellent thermal and alkaline stability under operating conditions of a fuel cell. Since synthesis and characterization of the full range of possible derivatives are very demanding, the computational study of the bond dissociation energy (BDE) between cyclopentadienyl (Cp) and CoCp+ in cobaltocenium (CoCp2+) may provide useful guidance to experiments. Here, we present a potentially more efficient approach for estimating the BDEs based-on the electronic structure enhanced by the machine learning (ML) techniques. In this study, we perform density functional theory (DFT) calculations for mono and multi substituted derivatives of CoCp2+ in an aqueous medium using B3LYP-D3 functional with m6-31g*/6-31g** basis set combination. Within the aqueous environment, the positive charge of the metal cation is neutralized by the hydroxide anion through the formation of the CoCp2OH complexes. The derivatives include a variety of electron-donating and electron-withdrawing groups as substituents to both Cps in the CoCp2+ cation. We have identified that BDEs correlate with a few chemical and machine-learnable descriptors related to the electron withdrawing/ donating character of the substituents, leading towards a predictive ML model of the BDEs as measures of stability of the metal cations in polyelectrolytes.

Correlating stability of substituted cobaltocenium [bis(cyclopentadienyl)cobalt(III)] with the computed molecular properties

ACS Spring Meeting 2021

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