Fast Na+ Ion Conduction in NASICON-Type Na3.4Sc2(SiO4)0.4(PO4)2.6 Observed by 23Na NMR Relaxometry

Kaus, Maximilian; Guin, Marie; Yavuz, Metin; Knapp, Michael; Tietz, Frank; Guillon, Olivier; Ehrenberg, Helmut; Indris, Sylvio

doi:10.1021/acs.jpcc.6b10523

Cited by 36 publications

(18 citation statements)

References 31 publications

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“…The very low activation energy E a,NMR of 0.15 eV (see also ref. 43 for comparison) extracted from the diffusion-induced NMR rate peak R 1 (1/ T ) seen at lower T (Fig. 2c) indicates that 23 Na NMR SLR senses the elementary diffusion processes to which local, correlated motions do also contribute.…”

Section: Resultsmentioning

confidence: 92%

Fast Na ion transport triggered by rapid ion exchange on local length scales

Lunghammer

Prutsch

Breuer

et al. 2018

Sci Rep

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The realization of green and economically friendly energy storage systems needs materials with outstanding properties. Future batteries based on Na as an abundant element take advantage of non-flammable ceramic electrolytes with very high conductivities. Na3Zr2(SiO4)2PO4-type superionic conductors are expected to pave the way for inherently safe and sustainable all-solid-state batteries. So far, only little information has been extracted from spectroscopic measurements to clarify the origins of fast ionic hopping on the atomic length scale. Here we combined broadband conductivity spectroscopy and nuclear magnetic resonance (NMR) relaxation to study Na ion dynamics from the µm to the angstrom length scale. Spin-lattice relaxation NMR revealed a very fast Na ion exchange process in Na3.4Sc0.4Zr1.6(SiO4)2PO4 that is characterized by an unprecedentedly high self-diffusion coefficient of 9 × 10−12 m2s−1 at −10 °C. Thus, well below ambient temperature the Na ions have access to elementary diffusion processes with a mean residence time τNMR of only 2 ns. The underlying asymmetric diffusion-induced NMR rate peak and the corresponding conductivity isotherms measured in the MHz range reveal correlated ionic motion. Obviously, local but extremely rapid Na+ jumps, involving especially the transition sites in Sc-NZSP, trigger long-range ion transport and push ionic conductivity up to 2 mS/cm at room temperature.

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Section: Resultsmentioning

confidence: 92%

Fast Na ion transport triggered by rapid ion exchange on local length scales

Lunghammer

Prutsch

Breuer

et al. 2018

Sci Rep

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“…Following validation analysis, models of each feature set size with high CVAC scores and low standard deviations were evaluated using an unseen test set, consisting of 19 NASICON compounds from literature with documented room-temperature ionic conductivities and an additional 38 LISICON compounds added as an evaluation of the model's extrapolation capabilities [51][52][53][54][55][56][57][58][59][60][61][62][63]. These lithium compounds were added due to the similar behavior of the two systems in electrochemical storage applications and as a test of the generalization capabilities of our model beyond its original training domain.…”

Section: Discussionmentioning

confidence: 99%

Machine learning-assisted cross-domain prediction of ionic conductivity in sodium and lithium-based superionic conductors using facile descriptors

Zong

Hippalgaonkar

2020

J. Phys. Commun.

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Solid state lithium-and sodium-ion batteries utilize solid ionically conducting compounds as electrolytes. However, the ionic conductivity of such materials tends to be lower than their liquid counterparts, necessitating research efforts into finding suitable alternatives. The process of electrolyte screening is often based on a mixture of domain expertise and trial-and-error, both of which are time and resource-intensive. In this work, we present a novel machine-learning based approach to predict the ionic conductivity of sodium and lithium-based SICON compounds. Using primarily theoretical elemental feature descriptors derivable from tabulated information on the unit cell and the atomic properties of the components of a target compound on a limited dataset of 70 NASICON-examples, we have designed a logistic regression-based model capable of distinguishing between poor and good superionic conductors with a validation accuracy of over 84%. Moreover, we demonstrate how such a system is capable of cross-domain classification on lithium-based examples with the same accuracy, despite being introduced to zero lithium-based compounds during training. Through a systematic permutation-based evaluation process, we reduced the number of considered features from 47 to 7, reduction of over 83%, while simultaneously improving model performance. The contributions of different electronic and structural features to overall ionic conductivity is also discussed, and contrasted with accepted theories in literature. Our results demonstrate the utility of such a facile tool in providing opportunities for initial screening of potential candidates as solid-state electrolytes through the use of existing data examples and simple tabulated or calculated features, reducing the time-to-market of such materials by helping to focus efforts on promising candidates. Given enough data utilizing suitable descriptors, high accurate cross-domain classifiers could be created for experimentalists, improving laboratory and computational efficiency.

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“…Nuclear magnetic resonance (NMR) refers to the phenomenon that the nucleus resonates between the energy levels under the action of an external magnetic field, which is a powerful analysis method that can provide abundant information about structure changes and metastable, short‐lived phases for the interface in ASSBs. [ 75 ] It is based on the interaction of the studied nuclear and the external magnetic field under the action of electromagnetic pulse in a certain frequency. A nucleus will keep spin precession when it is placed into an external magnetic field.…”

Section: Characterization Techniques For Interface In All‐solid‐statementioning

confidence: 99%

Advanced Characterization Techniques for Interface in All‐Solid‐State Batteries

Gao

et al. 2020

Small Methods

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The interface is a critical factor of electrochemical performance for all‐solid‐state batteries (ASSBs). Comprehending the composition, structure, morphology, and their evolution in the interface during charge/discharge cycling is greatly important for the development and practical application of ASSBs. The characterization techniques are very crucial and powerful for investigating the interface properties of ASSBs. This review briefly describes how the interface is generated in ASSBs, and then emphatically summarizes some important advanced techniques used to characterize the interface in ASSBs. The principle, advantage, and application of the techniques in the interface investigation are systematically discussed. This review provides fundamental insights and perspectives for developing the characterization techniques to deeply understand the interfacial behaviors of ASSBs, which is valuable for accelerating the commercialization of ASSBs used in electronic devices, electric vehicles, and large‐scale energy storage.

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Fast Na⁺ Ion Conduction in NASICON-Type Na_3.4Sc₂(SiO₄)_0.4(PO₄)_2.6 Observed by ²³Na NMR Relaxometry

Cited by 36 publications

References 31 publications

Fast Na ion transport triggered by rapid ion exchange on local length scales

Fast Na ion transport triggered by rapid ion exchange on local length scales

Machine learning-assisted cross-domain prediction of ionic conductivity in sodium and lithium-based superionic conductors using facile descriptors

Advanced Characterization Techniques for Interface in All‐Solid‐State Batteries

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