A First-Principles Study on Proton Conductivity of Acceptor-Doped Tin Pyrophosphate

Advanced Energy Materials

Jiang

et al. 2022

Self Cite

A significant hydration and protonic conduction in La2NiO4+δ‐based Ruddlesden–Popper (RP) oxides will enable their use as positrode materials in proton ceramic electrochemical cells (PCECs). Unlike perovskite electrolytes and positrode materials, where protons reside and jump on regular oxide ions, RP oxides such as La2NiO4+δ may contain oxygen interstitials and offer the possibility that protons locate on and migrate by help of these, suggesting a mechanism by which positrodes may operate by triple conduction. Here, theoretical calculations that support proton migration between interstitial oxide ions in the rock‐salt layer via unshared oxide ions in the perovskite layer are reported. Furthermore, water contents of pristine La2NiO4+δ and La2‐xAxNiO4+δ (A = Ca, Sr, Ba) are reported, showing that hydration increases with the level and basicity of the dopant. Hebb‐Wagner blocking electrode measurements show significant partial protonic conductivity in all samples. The effects of doping on hydration and of pH2O on the p‐type conductivity suggest that protons reside primarily on structural oxide ion, but a role of interstitial oxide ions for dissolution and migration of protons cannot be ruled out in cases with oxygen excess. The results are instructive for further studies and optimization of RP oxides as potential positrode materials for PCECs.

Section: Proton Sites In La 2 Nio 4 Without Interstitial Oxide Ionsmentioning

confidence: 99%

Protonic Conduction in La₂NiO₄₊_δ and La_2‐_xA_xNiO₄₊_δ (A = Ca, Sr, Ba) Ruddlesden–Popper Type Oxides

Zhong

Advanced Energy Materials

Jiang

et al. 2022

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“…The feasible set is the grid points on a 20×10×8 grid (grid interval: 0.25 ~ 0.3 Å), from which the grid points close to the host atoms are excluded, leading to 921 grid points in total. The second type of search space has lower dimensions by exploiting prior knowledge on protons in oxides, i.e., an OH bond formation in oxides [38][39][40][41][42][43][44][45]. Specifically, a spherical grid around an oxygen ion is introduced with the radius of 1 Å.…”

Section: A Definition Of the Feasible Setsmentioning

confidence: 99%

“…Figure 6 shows all proton sites (local energy minima) found by exhaustive local optimizations at all grid points. They all reside around oxygen ions with forming an OH bond, as is the case with protons in oxides [38][39][40][41][42][43][44][45]. There are four proton sites per oxygen ion, which are located near the vertical bisector of the two nearest zirconium ions from the oxygen ion.…”

Section: B Proton Sites In O-srzromentioning

confidence: 99%

“…The efficiency for exploring interstitial sites can be improved if some reasonable prior knowledge is available. The second feasible set defined on the spherical grid around an oxygen ion is based on the OH bond formation of protons in oxides [38][39][40][41][42][43][44][45]. In o-SrZrO 3 , there are two types of oxygen ions (O1 and O2), and the spherical grid around the O1 ion can be additionally reduced to the semi-spherical grid due to the mirror symmetry.…”

Section: B Proton Sites In O-srzromentioning

confidence: 99%

See 1 more Smart Citation

Machine-learning-based sampling method for exploring local energy minima of interstitial species in a crystal

Kanayama

2020

Phys. Rev. B

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An efficient machine-learning-based method combined with a conventional local optimization technique has been proposed for exploring local energy minima of interstitial species in a crystal. In the proposed method, an effective initial point for local optimization is sampled at each iteration from a given feasible set in the search space. The effective initial point is here defined as the grid point that most likely converges to a new local energy minimum by local optimization and/or is located in the vicinity of the boundaries between energy basins. Specifically, every grid point in the feasible set is classified by the predicted label indicating the local energy minimum that the grid point converges to.The classifier is created and updated at every iteration using the already-known information on the local optimizations at the earlier iterations, which is based on the support vector machine (SVM). The SVM classifier uses our original kernel function designed as reflecting the symmetries of both host crystal and interstitial species. The most distant unobserved point on the classification boundaries from the observed points is sampled as the next initial point for local optimization. The proposed method is applied to three model cases, i.e., the six-hump camelback function, a proton in strontium zirconate with the orthorhombic perovskite structure, and a water molecule in lanthanum sulfate with the monoclinic structure, to demonstrate the performance of the proposed method.

“…In addition, the NEB method requires huge computational costs for low-symmetry crystals, even if only the MEPs in the PES are evaluated. For example, in our recent study on proton conduction in tin pyrophosphate (SnP 2 O 7 ) with space group of P2 1/C , we evaluated 143 possible elementary paths connecting 15 local energy minima by the NEB method [11]. An alternative method that is both robust and efficient is desirable to analyze complicated atomic transfers consisting of many elementary paths in a low-symmetry crystal.…”

mentioning

confidence: 99%

Potential Energy Surface Mapping of Charge Carriers in Ionic Conductors Based on a Gaussian Process Model

Takeuchi

2018

Nanoinformatics

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The potential energy surface (PES) of a charge carrier in a host crystal is an important concept to fundamentally understand ionic conduction. Such PES evaluations, especially by density functional theory (DFT) calculations, generally require vast computational costs. This chapter introduces a novel selective sampling procedure to preferentially evaluate the partial PES characterizing ionic conduction. This procedure is based on a machine learning method called the Gaussian process (GP), which reduces computational costs for PES evaluations. During the sampling procedure, a statistical model of the PES is constructed and sequentially updated to identify the region of interest characterizing ionic conduction in configuration space. Its efficacy is demonstrated using a model case of proton conduction in a well-known proton-conducting oxide, barium zirconate (BaZrO 3 ) with the cubic perovskite structure. The proposed procedure efficiently evaluates the partial PES in the region of interest that characterizes proton conduction in the host crystal lattice of BaZrO 3 .