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Intercalation electrode materials reversibly insert cations into their lattices under applied potentials or currents, which can be used to perform electrochemical separations. Optimizing performance, however, remains challenging due to tradeoffs between selectivity and separation rate being influenced by multiple variables. This study developed a quantitative model to describe the current response and cation selectivity of an intercalation electrode in a binary cation solution during cyclic voltammetry. We hypothesized that current responses and selectivity could be calculated by summing individual ion contributions. Cyclic voltammograms were experimentally measured using nickel hexacyanoferrate electrodes in NaCl, KCl, or mixed solutions. Post-mortem electron probe micro-analysis quantified intercalated Na+and K+fractions. A one-dimensional finite element model incorporating the Nernst-Frumkin isotherm, Butler-Volmer kinetics, and ion diffusion was developed, parameterized with pure NaCl or KCl solutions, and validated against mixed solutions. The model accurately reproduced experimental cyclic voltammograms and ion partitioning behaviors at ionic strengths ≥0.2 mol·l−1. However, at lower ionic strengths, significant discrepancies arose for reasons still unclear. Results indicate that modeling ion contributions individually effectively captures the electrochemical response of selective intercalation electrodes at sufficiently high ionic strengths.
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Effects of interstitial water and alkali cations on the expansion, intercalation potential, and orbital coupling of nickel hexacyanoferrate from first principles
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.
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- Award ID(s):
- 1931659
- PAR ID:
- 10363845
- Publisher / Repository:
- American Institute of Physics
- Date Published:
- Journal Name:
- Journal of Applied Physics
- Volume:
- 131
- Issue:
- 10
- ISSN:
- 0021-8979
- Page Range / eLocation ID:
- Article No. 105101
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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