Toward all-dislocation-ceramics for high ionic conductivity produced by dry pressing at relatively low temperatures with and without ultrasound

Yasui, Kyuichi; Hamamoto, Koichi

doi:10.1063/5.0185747

Cited by 3 publications

(3 citation statements)

References 128 publications

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“…In many ODE models, the independent variable is time, and physical quantities such as density, mobile and immobile dislocation densities, etc. are assumed to be spatially uniform [ 24 , 66 , 144 , 145 , 146 ]. As a result, equations describing some physical phenomena could contain only derivatives with respect to the single variable (time), which are ordinary differential equations (ODEs).…”

Section: Physical or Phenomenological Ode Modelsmentioning

confidence: 99%

“…The higher fracture toughness of solid electrolytes could prevent dendrite formation and short-circuiting [ 58 ]. We call such a possible new type of solid electrolytes all-dislocation-ceramics [ 58 , 66 ]. We have also shown theoretically that all-dislocation-ceramics could be produced by dry pressing, which is sintering at relatively low temperatures under high applied pressure [ 66 ].…”

Section: Introductionmentioning

confidence: 99%

“…We call such a possible new type of solid electrolytes all-dislocation-ceramics [ 58 , 66 ]. We have also shown theoretically that all-dislocation-ceramics could be produced by dry pressing, which is sintering at relatively low temperatures under high applied pressure [ 66 ]. In the present review, it is pointed out that ML could not predict all-dislocation-ceramics as a candidate for materials with high ionic conductivity without dendrite formation, because ML hardly predicts experimental results beyond the parameter space of pre-existing experimental data.…”

Section: Introductionmentioning

confidence: 99%

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Merits and Demerits of Machine Learning of Ferroelectric, Flexoelectric, and Electrolytic Properties of Ceramic Materials

Yasui

2024

Materials

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In the present review, the merits and demerits of machine learning (ML) in materials science are discussed, compared with first principles calculations (PDE (partial differential equations) model) and physical or phenomenological ODE (ordinary differential equations) model calculations. ML is basically a fitting procedure of pre-existing (experimental) data as a function of various factors called descriptors. If excellent descriptors can be selected and the training data contain negligible error, the predictive power of a ML model is relatively high. However, it is currently very difficult for a ML model to predict experimental results beyond the parameter space of the training experimental data. For example, it is pointed out that all-dislocation-ceramics, which could be a new type of solid electrolyte filled with appropriate dislocations for high ionic conductivity without dendrite formation, could not be predicted by ML. The merits and demerits of first principles calculations and physical or phenomenological ODE model calculations are also discussed with some examples of the flexoelectric effect, dielectric constant, and ionic conductivity in solid electrolytes.

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Section: Physical or Phenomenological Ode Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Merits and Demerits of Machine Learning of Ferroelectric, Flexoelectric, and Electrolytic Properties of Ceramic Materials

Yasui

2024

Materials

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Tempo‐Spatial Manipulation of Ultrasonics Toward Healing of Solid‐State Batteries

Xiao,

Yao,

Sapkota

et al. 2024

Advanced Energy Materials

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Solid‐state batteries (SSBs) struggle with poor interface contact and the resulting fast dendrite growth and short circuits. Ultrasonic treatment offers a potential solution to heal SSBs by ultrasonic welding at room temperature to improve interface contact free from side reactions. A significant challenge is the adverse effect of ultrasonics on SSBs, such as severe cracking in brittle electrolytes increasing the risk of dendrite growth and short circuits. Inspired by ultrasonic treatment on patients in hospitals, a tempo‐spatial manipulation of ultrasonics (TSMU) is examined, guided by ultrasonic‐structure interaction simulations, to maximize the propagated energy for ultrasonic welding while mitigating the possibility of electrolyte cracking. The application of TSMU to Li/garnet/Li symmetric cells is found to reduce the interfacial resistance from thousands to tens of Ω cm−2 for poorly formed interfaces, from SSB manufacturing to (dis)charging cycles, which outperforms constant ultrasonic treatment that achieves a resistance minimum of hundreds of Ω cm−2; otherwise, a short circuit occurs due to the cracking. Galvanostatic cycling further demonstrates the excellent stability of TSMU‐healed cells over 1,000 cycles at a current density of 0.1 mA cm−2, which is not attainable by constant ultrasonics with short‐circuiting issues.

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Soft Matter Electrolytes: Mechanism of Ionic Conduction Compared to Liquid or Solid Electrolytes

Yasui,

Hamamoto

2024

Materials

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Soft matter electrolytes could solve the safety problem of widely used liquid electrolytes in Li-ion batteries which are burnable upon heating. Simultaneously, they could solve the problem of poor contact between electrodes and solid electrolytes. However, the ionic conductivity of soft matter electrolytes is relatively low when mechanical properties are relatively good. In the present review, mechanisms of ionic conduction in soft matter electrolytes are discussed in order to achieve higher ionic conductivity with sufficient mechanical properties where soft matter electrolytes are defined as polymer electrolytes and polymeric or inorganic gel electrolytes. They could also be defined by Young’s modulus from about Pa to Pa. Many soft matter electrolytes exhibit VFT (Vogel–Fulcher–Tammann) type temperature dependence of ionic conductivity. VFT behavior is explained by the free volume model or the configurational entropy model, which is discussed in detail. Mostly, the amorphous phase of polymer is a better ionic conductor compared to the crystalline phase. There are, however, some experimental and theoretical reports that the crystalline phase is a better ionic conductor. Some methods to increase the ionic conductivity of polymer electrolytes are discussed, such as cavitation under tensile deformation and the microporous structure of polymer electrolytes, which could be explained by the conduction mechanism of soft matter electrolytes.

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Toward all-dislocation-ceramics for high ionic conductivity produced by dry pressing at relatively low temperatures with and without ultrasound

Cited by 3 publications

References 128 publications

Merits and Demerits of Machine Learning of Ferroelectric, Flexoelectric, and Electrolytic Properties of Ceramic Materials

Merits and Demerits of Machine Learning of Ferroelectric, Flexoelectric, and Electrolytic Properties of Ceramic Materials

Tempo‐Spatial Manipulation of Ultrasonics Toward Healing of Solid‐State Batteries

Soft Matter Electrolytes: Mechanism of Ionic Conduction Compared to Liquid or Solid Electrolytes

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