Computational characterization of lightweight multilayer MXene Li-ion battery anodes

Authors:
Publisher website
Google Scholar
Abstract
MXenes, a class of two-dimensional transition metal carbides and nitrides, have shown promise experimentally and computationally for use in energy storage applications. In particular, the most lightweight members of the monolayer MXene family (M = Sc, Ti, V, or Cr) are predicted to have gravimetric capacities above 400 mAh/g, higher than graphite. Additionally, intercalation of ions into multilayer MXenes can be accomplished at low voltages, and low diffusion barriers exist for Li diffusing across monolayer MXenes. However, large discrepancies have been observed between the calculated and experimental reversible capacities of MXenes. Here, dispersion-corrected density functional theory calculations are employed to predict reversible capacities and other battery-related properties for six of the most promising members of the MXene family (O-functionalized Ti- and V-based carbide MXenes) as bilayer structures. The calculated reversible capacities of the V2CO2 and Ti2CO2 bilayers agree more closely with experiment than do previous calculations for monolayers. Additionally, the minimum energy paths and corresponding energy barriers along the in-plane [1000] and [0100] directions for Li travelling between neighboring MXene layers are determined. V4C3O2 exhibits the lowest diffusion barrier of the compositions considered, at 0.42 eV, but its reversible capacity (148 mAh/g) is dragged down by its heavy formula unit. Conversely, the V2CO2 MXene shows good reversible capacity (276 mAh/g), but a high diffusion barrier (0.82 eV). We show that the diffusion barriers of all bilayer structures are significantly higher than those calculated for the corresponding monolayers, advocating the use of dispersed monolayer MXenes instead of multilayers in high performance anodes.
Funding Information
  • National Science Foundation (ACI-1440547, DMR-1056587, DMR-1307840)

This publication has 48 references indexed in Scilit: