Electrical Response of Ionic Conductors

Habasaki, Junko; León, Carlos; Ngai, K. L.

doi:10.1007/978-3-319-42391-3_4

Cited by 2 publications

(1 citation statement)

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“…Ngai et al , and Li. et al proposed that the observed non-Arrhenius dependence of the conductivity was due to the glassy nature − for 8YSZ using Vogel–Fulcher–Tammann law . The barrier reduction from low to high temperature has been tentatively explained by many groups ,,,, as the consequence of, for example, the likely Y–O v pairing below 900 K that constrains the motion of O v , and the effect of the grain boundary on conductivity at low temperatures. , These, however, were not evidenced directly by simulations.…”

Section: Introductionmentioning

confidence: 99%

Resolving the Temperature and Composition Dependence of Ion Conductivity for Yttria-Stabilized Zirconia from Machine Learning Simulation

2020

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The temperature and composition dependence of the ion conductivity for yttria-stabilized zirconia (YSZ) have been hotly studied over the past 50 years. Due to the sluggish oxygen anion diffusion and the low doping of oxide, the computation of ion conductivity traditionally has to be performed with empirical force field potentials in order to achieve the required long timescale, which, however, fails to reproduce some critical observations in experiment, e.g., the conductivity maximum achieved at 8 mol % YSZ at the operating temperatures. Here by using our recently developed Y−Zr−O global neural network (G-NN) potential, we are able to carry out a series of long-time molecular dynamics simulations for YSZ at 6.7, 8, 10, and 14.3 mol % over a wide temperature range (800−2000 K). This finally quantitatively resolves the effects of temperature and composition on the ion conductivity. We confirm the key experimental findings that (i) 8 mol % YSZ has the highest ion conductivity, 0.16−0.51 S/cm, at 1200−1600 K, agreeing with 0.16−0.55 S/cm in experiment, and the maximum conductivity shifts to the higher Y composition above 1600 K and (ii) over the wide temperature range (800−2000 K) the ion conductivity of 8YSZ exhibits the non-Arrhenius behaviors with two different activation energies. The physical origin for these peculiar phenomena is revealed at the atomic level by analyzing the MD pathways. The presence of monoclinic phase and the aggregation of oxygen vacancy along ⟨112⟩ are two key factors to retard oxygen diffusion. For 8 mol % YSZ, the oxygen movement is dominated by local vibrations below 1000 K but becomes delocalized above 1000 K, which results in the gradual aggregation of oxygen vacancy along a new ⟨112⟩ direction. Our results demonstrate that the G-NN potential from unbiased machine learning of the global potential energy surface can meet the high standard in both accuracy and speed required for material simulation.

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Section: Introductionmentioning

confidence: 99%

Resolving the Temperature and Composition Dependence of Ion Conductivity for Yttria-Stabilized Zirconia from Machine Learning Simulation

2020

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The persistence of memory in ionic conduction probed by nonlinear optics

Poletayev,

Hoffmann,

Dawson

et al. 2024

Nature

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Predicting practical rates of transport in condensed phases enables the rational design of materials, devices and processes. This is especially critical to developing low-carbon energy technologies such as rechargeable batteries1–3. For ionic conduction, the collective mechanisms4,5, variation of conductivity with timescales6–8 and confinement9,10, and ambiguity in the phononic origin of translation11,12, call for a direct probe of the fundamental steps of ionic diffusion: ion hops. However, such hops are rare-event large-amplitude translations, and are challenging to excite and detect. Here we use single-cycle terahertz pumps to impulsively trigger ionic hopping in battery solid electrolytes. This is visualized by an induced transient birefringence, enabling direct probing of anisotropy in ionic hopping on the picosecond timescale. The relaxation of the transient signal measures the decay of orientational memory, and the production of entropy in diffusion. We extend experimental results using in silico transient birefringence to identify vibrational attempt frequencies for ion hopping. Using nonlinear optical methods, we probe ion transport at its fastest limit, distinguish correlated conduction mechanisms from a true random walk at the atomic scale, and demonstrate the connection between activated transport and the thermodynamics of information.

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Electrical Response of Ionic Conductors

Cited by 2 publications

References 388 publications

Resolving the Temperature and Composition Dependence of Ion Conductivity for Yttria-Stabilized Zirconia from Machine Learning Simulation

Resolving the Temperature and Composition Dependence of Ion Conductivity for Yttria-Stabilized Zirconia from Machine Learning Simulation

The persistence of memory in ionic conduction probed by nonlinear optics

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