Strategies and practical approaches for stable and high energy density sodium-ion battery: a step closer to commercialization

Yadav, Poonam; Patrike, Apurva; Wasnik, Kundan; Shelke, Vilas; Shelke, Manjusha V.

doi:10.1016/j.mtsust.2023.100385

Cited by 13 publications

(9 citation statements)

References 252 publications

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“…Therefore, SIBs exhibit a much lower energy density than LIBs. 132,133 Increasing the working potential window is one way to achieve a higher energy density in SIBs. Nevertheless, layered oxide cathodes are prone to severe irreversible phase transitions in the high charge state, resulting in a deteriorated performance.…”

Section: Challenges -Structure Characteristic and Failure Mechanismmentioning

confidence: 99%

Routes to high-performance layered oxide cathodes for sodium-ion batteries

Wang,

Zhu,

et al. 2024

Chem. Soc. Rev.

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Section: Challenges -Structure Characteristic and Failure Mechanismmentioning

confidence: 99%

Routes to high-performance layered oxide cathodes for sodium-ion batteries

Wang,

Zhu,

et al. 2024

Chem. Soc. Rev.

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“…In efforts to find the SIB cathodes with promising electrochemical performance, materials such as transition metal oxide, polyanionic compounds, and Prussian blue analogues have been extensively researched. , Particularly, because of its excellent structural stability and high voltage characteristics, Na super ionic conductor (NASICON), a type of polyanionic compound, stands out as a promising material for achieving superior energy density and reduced volume variation . NASICON has the chemical formula Na x M 2 (AO 4 ) 3 (where M = metal, such as V, Fe, Mn, Co, etc., and A = polyanion central site, such as P, S, Si, Mo, etc.…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning-Driven High-Throughput Screening for High-Energy Density and Stable NASICON Cathodes

Jeong,

Kim,

Sun

et al. 2024

ACS Appl. Mater. Interfaces

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The Na super ionic conductor (NASICON), which has outstanding structural stability and a high operating voltage, is an appealing material for overcoming the limits of low specific energy and larger volume distortion of sodium-ion batteries. In this study, to discover ideal NASICON cathode materials, a screening platform based on density functional theory (DFT) calculations and machine learning (ML) is developed. A training database was generated utilizing the previous 124 545 electrode databases, and a test set of 3126 potential NASICON structures [Na x M y M′ 1−y (PO 4 ) 3 ] with 27 dopants at the metal site and 6 dopants at the polyanion central site was constructed. The developed ML surrogate model identifies 796 materials that satisfy the following criteria: formation energy of <0.0 eV/atom, energy above hull of ≤0.025 eV/atom, volume change of ≤4%, and theoretical capacity of ≥50 mAh/g. The thermodynamically stable configurations of doped NASICON structures were then selected using machine learning interatomic potential (MLIP), enabling rapid consideration of various dopant site configurations. DFT calculations are followed on 796 screened materials to obtain energy density, average voltage, and volume change. Finally, 50 candidates with an average voltage of ≥3.5 V are identified. The suggested platform accelerates the exploration for optimal NASICON materials by narrowing the focus on materials with desired properties, saving considerable resources.

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“…In view of this problem, sodium-ion batteries (SIBs) present several advantages. Among them, sodium resources have wide geographic availability and low cost and can be safely transported in their discharge state, and there are similar physicochemical properties between the two alkali Na and Li metals [ 3 , 4 , 5 ]. A SIB comprises a positive electrode made of a material commonly containing Na, a negative electrode material that does not necessarily contain Na, an electrolyte (whether liquid or solid), as well as a separator [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

“…Meanwhile, the current is flowing in the opposite direction via an external circuit. Similarly, during the discharge or sodiation of the positive electrode, sodium ions are moving from the negative electrode to go back into the positive electrode [ 4 ]. This reversible intercalation process of ions into host electrode materials is referred to as the rocking-chair working principle of battery electrochemical energy storage systems [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

“…This reversible intercalation process of ions into host electrode materials is referred to as the rocking-chair working principle of battery electrochemical energy storage systems [ 7 ]. Further, the material synthesis methods of sodium compounds can easily be borrowed and adapted from lithium compounds [ 4 ]. The pouch, prismatic, or cylindrical geometrical configurations used for LIBs can be further employed for manufacturing SIBs.…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Methodology Combining K-Means Machine Learning and Electrochemical Modelling for the Determination of Ionic Diffusivity and Kinetic Properties in Battery Electrodes

Capron

Couto

2023

Materials

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This paper presents an innovative and efficient methodology for the determination of the solid-state diffusion coefficient in electrode materials with phase transitions for which the assumption of applying the well-known formula from the work of Weppner et al. is not satisfied. This methodology includes a k-means machine learning screening of Galvanostatic Intermittent Titration Technique (GITT) steps, whose outcomes feed a physics-informed algorithm, the latter involving a pseudo-two-dimensional (P2D) electrochemical model for carrying out the numerical simulations. This methodology enables determining, for all of the 47 steps of the GITT characterization, the dependency of the Na+ diffusion coefficient as well as the reaction rate constant during the sodiation of an NVPF electrode to vary between 9 × 10−18 and 6.8 × 10−16 m2·s−1 and between 2.7 × 10−14 and 1.5 × 10−12 m2.5·mol−0.5·s−1, respectively. This methodology, also validated in this paper, is (a) innovative since it presents for the first time the successful application of unsupervised machine learning via k-means clustering for the categorization of GITT steps according to their characteristics in terms of voltage; (b) efficient given the considerable reduction in the number of iterations required with an average number of iterations equal to 8, and given the fact the entire experimental duration of each step should not be simulated anymore and hence can be simply restricted to the part with current and a small part of the rest period; (c) generically applicable since the methodology and its physics-informed algorithm only rely on “if” and “else” statements, i.e., no particular module/toolbox is required, which enables its replication and implementation for electrochemical models written in any programming language.

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Strategies and practical approaches for stable and high energy density sodium-ion battery: a step closer to commercialization

Cited by 13 publications

References 252 publications

Routes to high-performance layered oxide cathodes for sodium-ion batteries

Routes to high-performance layered oxide cathodes for sodium-ion batteries

Machine-Learning-Driven High-Throughput Screening for High-Energy Density and Stable NASICON Cathodes

An Efficient Methodology Combining K-Means Machine Learning and Electrochemical Modelling for the Determination of Ionic Diffusivity and Kinetic Properties in Battery Electrodes

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