Alkali Metal Cation and Proton Conductors: Relationships between Composition, Crystal Structure, and Properties

Avdeev, Maxim; Nalbandyan, V. B.; Shukaev, Igor L.

doi:10.1002/9783527627868.ch7

Cited by 14 publications

(10 citation statements)

References 277 publications

(335 reference statements)

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“…It has been confirmed that the high electronegativity of the octahedral cations in oxides is the main factor stabilizing prismatic structures. 35,36 The electronegativity of Mn 2+ (1.436) is higher than that of Mg (1.208). 37 It can be understood that the heavily Mn 2+ -doped Na 2 CaMg(PO 4 ) 2 could be stable with the prismatic structure (b-phase).…”

Section: Discussionmentioning

confidence: 98%

Phase formations and tunable red luminescence of Na2CaMg1−xMnx(PO4)2 (x = 0.05–1.0)

Lü

Zhu

et al. 2011

J. Mater. Chem.

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Section: Discussionmentioning

confidence: 98%

Phase formations and tunable red luminescence of Na2CaMg1−xMnx(PO4)2 (x = 0.05–1.0)

Lü

Zhu

et al. 2011

J. Mater. Chem.

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“…For the majority of the materials mentioned in Ref. [] no crystallographic data was collected. Especially for the materials systematised under the ‘Brucite‐like’ structures, conductivity data is presented without structural information.…”

Section: Methodsmentioning

confidence: 99%

“…As taken from Ref. [], the best‐conducting crystalline solid Na‐ion electrolytes known so far can be categorised into three different main groups (β‐alumina, NaSICon and Na rare‐earth silicates) showing very similar, comparably high conductivities for Na + (approx. 10 −3 S/cm at room temperature, around (10 −2 –10 0 ) S/cm at 300 °C).…”

Section: Introductionmentioning

confidence: 99%

Identification of solid oxygen‐containing Na‐electrolytes: An assessment based on crystallographic and economic parameters

Meutzner

Münchgesang

Leisegang

et al. 2016

Crystal Research and Technology

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We have scanned the inorganic crystal structure database using Voronoi-Dirichlet methodology for inorganic, crystalline, solid Na electrolytes and applied a total of nine different crystallographic and economic parameters in order to evaluate the potential of each material. Especially for stationary electrochemical energy storage -used to counteract the capricious nature of renewable energy sources and momentary variations of energy consumption -Nabased chemistries have a considerable market share. They rely on solid Na electrolytes separating the Na and S electrode compartments. We used data generated from the currently widest-spread Na electrolytes to lay a foundation for the crystallographic data mining and Voronoi-Dirichlet partitioning of the database. The structural data is the basis for the calculation of the above-mentioned parameters. We introduced an evaluation and scoring scheme to systematise the results and -depending on the weighting scheme -point towards the most promising materials. Aluminosilicates and transition metal oxides seem especially interesting but, depending on the weighting, any of the more than 400 candidates could be the next-generation solid Na electrolyte.

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“…Polarizable anion frameworks or anisotropic polarizability of the anions enhance the migration of Li ions [21,22]. The chemical properties further depend on the material, structural and experimental properties mentioned above [23,24].…”

Section: Introductionmentioning

confidence: 99%

Essential structural and experimental descriptors for bulk and grain boundary conductivities of Li solid electrolytes

Tanaka

Komori

et al. 2020

Science and Technology of Advanced Materials

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We present a computational approach for identifying the important descriptors of the ionic conductivities of lithium solid electrolytes. Our approach discriminates the factors of both bulk and grain boundary conductivities, which have been rarely reported. The effects of the interrelated structural (e.g. grain size, phase), material (e.g. Li ratio), chemical (e.g. electronegativity, polarizability) and experimental (e.g. sintering temperature, synthesis method) properties on the bulk and grain boundary conductivities are investigated via machine learning. The data are trained using the bulk and grain boundary conductivities of Li solid conductors at room temperature. The important descriptors are elucidated by their feature importance and predictive performances, as determined by a nonlinear XGBoost algorithm: (i) the experimental descriptors of sintering conditions are significant for both bulk and grain boundary, (ii) the material descriptors of Li site occupancy and Li ratio are the prior descriptors for bulk, (iii) the density and unit cell volume are the prior structural descriptors while the polarizability and electronegativity are the prior chemical descriptors for grain boundary, (iv) the grain size provides physical insights such as the thermodynamic condition and should be considered for determining grain boundary conductance in solid polycrystalline ionic conductors.

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Alkali Metal Cation and Proton Conductors: Relationships between Composition, Crystal Structure, and Properties

Cited by 14 publications

References 277 publications

Phase formations and tunable red luminescence of Na2CaMg1−xMnx(PO4)2 (x = 0.05–1.0)

Phase formations and tunable red luminescence of Na2CaMg1−xMnx(PO4)2 (x = 0.05–1.0)

Identification of solid oxygen‐containing Na‐electrolytes: An assessment based on crystallographic and economic parameters

Essential structural and experimental descriptors for bulk and grain boundary conductivities of Li solid electrolytes

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