Electrical Conductivity and Electrochemical Characteristics of Na3V2(PO4)3-Based NASICON-Type Materials

Новикова, С. А.; Larkovich, Roman V.; Chekannikov, Andrey; Кулова, Т. Л.; Скундин, А. М.; Yaroslavtsev, A. B.

doi:10.1134/s0020168518080149

Cited by 33 publications

(26 citation statements)

References 60 publications

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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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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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“…All electrochemical performances were tested in the voltage range of 0.05-3.0V at room temperature. parameters increased following introduction of Cr 3+ into the Li 2 ZnTi 3 O 8 crystal structure due to the larger radius of Cr 3+ (0.062 nm) compared with that of Ti 4+ (0.061 nm) (Novikova et al, 2018), which promotes the transportation of lithium ions and further enhances the electrochemical performance (Tai et al, 2020).…”

Section: Methodsmentioning

confidence: 99%

Cr-Doped Li2ZnTi3O8 as a High Performance Anode Material for Lithium-Ion Batteries

Zeng

Jing

Zhu³

et al. 2021

Front. Chem.

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Li2ZnTi2.9Cr0.1O8 and Li2ZnTi3O8 were synthesized by the liquid phase method and then studied comparatively using X-ray diffraction (XRD), scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), galvanostatic charge–discharge testing, cyclic stability testing, rate performance testing, and electrochemical impedance spectroscopy (EIS). The results showed that Cr-doped Li2ZnTi3O8 exhibited much improved cycle performance and rate performance compared with Li2ZnTi3O8. Li2ZnTi2.9Cr0.1O8 exhibited a discharge ability of 156.7 and 107.5 mA h g−1 at current densities of 2 and 5 A g−1, respectively. In addition, even at a current density of 1 A g−1, a reversible capacity of 162.2 mA h g−1 was maintained after 200 cycles. The improved electrochemical properties of Li2ZnTi2.9Cr0.1O8 are due to its increased electrical conductivity.

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“…Besides, for low-temperature conduction, the activation energy is decreased from 84 ± 2 to 54 ± 1 kJ/mol. At the elevated charge rate, the more porous material retains its high level of the theoretical capacity [89].…”

Section: Nasicon-type Materials Based On Na 3 V 2 (Po 4 )mentioning

confidence: 99%

NASICON Electrodes for Sodium-Ion Batteries

Kumar

2020

Materials Research Foundations

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The incessantly growing demand for energy storage is attracting the researcher's attention to develop consistent, effective, and ecologically harmless electrochemical systems for energy storage. Sodium-ion batteries (SIBs) are emergent as one of the utmost efficient large-scale systems for energy storage due to the great accessibility of raw sodium resources and their cost-effective assets. Sodium Super Ionic Conductor (NASICON) electrodes-based materials have offered an opportunity precisely for SIBs as a next-generation energy storage device because of their unique properties. NASICON electrodes have been extensively proven to demonstrate enhanced and miscellaneous features for SIBs in terms of high rate competence, flexible battery structures, long cycling life, and high specific capacity, due to their outstanding characteristics such as high charge carrier mobility, excellent mechanical strength, high theoretical ability, high electronic conductivity, and large surface area. This chapter summarizes the important advancement accomplished on NASICON-based electrodes for application in SIBs, including both cathodes and anodes over the past decade. Furthermore, this chapter also focuses on the new challenges and commercial demand for SIBs and some perspectives on the use of NASICON-based electrodes for future SIB applications.

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Electrical Conductivity and Electrochemical Characteristics of Na3V2(PO4)3-Based NASICON-Type Materials

Cited by 33 publications

References 60 publications

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

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

Cr-Doped Li2ZnTi3O8 as a High Performance Anode Material for Lithium-Ion Batteries

NASICON Electrodes for Sodium-Ion Batteries

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