Effects of interstitial water and alkali cations on the expansion, intercalation potential, and orbital coupling of nickel hexacyanoferrate from first principles

Author:

Liu Sizhe1,Smith Kyle C.1234ORCID

Affiliation:

1. Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA

2. Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA

3. Computational Science and Engineering Program, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA

4. Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA

Abstract

Prussian blue analogs (PBAs) are an important material class for aqueous electrochemical separations and energy storage owing to their ability to reversibly intercalate monovalent cations. However, incorporating interstitial [Formula: see text] molecules in the ab initio study of PBAs is technically challenging, though essential to understanding the interactions between interstitial water, interstitial cations, and the framework lattice that affect intercalation potential and cation intercalation selectivity. Accordingly, we introduce and use a method that combines the efficiency of machine-learning models with the accuracy of ab initio calculations to elucidate mechanisms of (1) lattice expansion upon intercalation of cations of different sizes, (2) selectivity bias toward intercalating hydrophobic cations of large size, and (3) semiconductor–conductor transitions from anhydrous to hydrated lattices. We analyze the PBA nickel hexacyanoferrate [[Formula: see text]] due to its structural stability and electrochemical activity in aqueous electrolytes. Here, grand potential analysis is used to determine the equilibrium degree of hydration for a given intercalated cation (Na[Formula: see text], K[Formula: see text], or Cs[Formula: see text]) and [Formula: see text] oxidation state based on pressure-equilibrated structures determined with the aid of machine learning and simulated annealing. The results imply new directions for the rational design of future cation-intercalation electrode materials that optimize performance in various electrochemical applications, and they demonstrate the importance of choosing an appropriate calculation framework to predict the properties of PBA lattices accurately.

Funder

National Science Foundation

Extreme Science and Engineering Discovery Environment

National Center for Supercomputing Applications

Publisher

AIP Publishing

Subject

General Physics and Astronomy

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