Characterization of Na1.3Al0.3Zr1.7(PO4)3 solid electrolyte ceramics by impedance spectroscopy

Kazakevičius, E.; Kežionis, A.; Žukauskaitė, L.; Barré, Maud; Салкус, Т.; Žalga, Artūras; Selskis, A.; Орлюкас, А.Ф.

doi:10.1016/j.ssi.2014.09.038

Cited by 6 publications

(3 citation statements)

References 28 publications

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“…The conductivity dispersion above 350 K can be attributed to Na + ion migration in the grains of ceramic samples as in [26][27][28]. The temperature dependences of bulk electrical conductivity (σ b ) of the ceramics were derived from Nyquist impedance plots (see inset of Fig.…”

Section: Resultsmentioning

confidence: 99%

Preparation, structure, surface and impedance analysis of Na₂Zn_0.5Mn_0.5P₂O₇ ceramics

Venckutė¹,

Dindune²,

Valdniece³

et al. 2017

Lith. J. Phys.

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The Na 2 Zn 0.5 Mn 0.5 P 2 O 7 powder was prepared by the solid state reaction method. The powder structure was studied by X-ray diffraction (XRD) in the temperature range from room temperature (RT) to 520 K. The results of XRD measurements show that the obtained Na 2 Zn 0.5 Mn 0.5 P 2 O 7 is a mixture of two phases: Na 2 MnP 2 O 7 , which crystallizes in the triclinic space group P -1, and Na 2 ZnP 2 O 7 , which crystallizes in the tetragonal space group P4 2 /mmm. The chemical compositions of the powder and ceramic samples were investigated by an energy dispersive X-ray spectrometer (EDX) and X-ray fluorescence spectroscopy (XFS). The surface of ceramics was examined by X-ray photoelectron spectroscopy (XPS). The electrical conductivity and dielectric permittivity of the ceramics were investigated from RT to 700 K in the frequency range 10-10 9 Hz. The relaxational dispersion of electrical conductivity in the investigated frequency and temperature range was found.

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Section: Resultsmentioning

confidence: 99%

Preparation, structure, surface and impedance analysis of Na₂Zn_0.5Mn_0.5P₂O₇ ceramics

Venckutė¹,

Dindune²,

Valdniece³

et al. 2017

Lith. J. Phys.

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show abstract

“…The third constant phase element (CPE3) reects the impedance response arising from the distribution of ionic and electronic polarization processes occurring at the pellet-electrode interface. 39 This equivalent sub-circuit conrms that the conductivity of the materials is a mixture of an ionic and electronic nature. 40 The total equivalent circuit used to t the impedance spectra is inset in Fig.…”

Section: Impedance Studymentioning

confidence: 99%

Insights into structure, morphology and conductivity of the earth-abundant NASICON phosphate, Na₄MnFe(PO₄)₃

Chayal,

El Arni,

Saadi

et al. 2024

RSC Adv.

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“…A training dataset consisting of 70 experimentally synthesized NASICON materials of R3c symmetry was collected from literature to generate a feature set containing experimentally measured ionic conductivity values, along with simple molecular descriptors, structural descriptors, and electronic descriptors. [5,[16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34] The separation of these descriptors was done by category, and is shown below in Table 1. The formulas of our NASICON materials and accompanying ionic conductivities are listed in Table S1a and S1b, while the methods of determination for each descriptor (henceforth feature) are listed in Table S1c.…”

Section: Training Setmentioning

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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Characterization of Na1.3Al0.3Zr1.7(PO4)3 solid electrolyte ceramics by impedance spectroscopy

Cited by 6 publications

References 28 publications

Preparation, structure, surface and impedance analysis of Na₂Zn_0.5Mn_0.5P₂O₇ ceramics

Preparation, structure, surface and impedance analysis of Na₂Zn_0.5Mn_0.5P₂O₇ ceramics

Insights into structure, morphology and conductivity of the earth-abundant NASICON phosphate, Na₄MnFe(PO₄)₃

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

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Characterization of Na1.3Al0.3Zr1.7(PO4)3 solid electrolyte ceramics by impedance spectroscopy

Cited by 6 publications

References 28 publications

Preparation, structure, surface and impedance analysis of Na2Zn0.5Mn0.5P2O7 ceramics

Preparation, structure, surface and impedance analysis of Na2Zn0.5Mn0.5P2O7 ceramics

Insights into structure, morphology and conductivity of the earth-abundant NASICON phosphate, Na4MnFe(PO4)3

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

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Preparation, structure, surface and impedance analysis of Na₂Zn_0.5Mn_0.5P₂O₇ ceramics

Preparation, structure, surface and impedance analysis of Na₂Zn_0.5Mn_0.5P₂O₇ ceramics

Insights into structure, morphology and conductivity of the earth-abundant NASICON phosphate, Na₄MnFe(PO₄)₃