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Rational Route for Increasing Intercalation Capacity of Hard Carbons as Sodium-Ion Battery Anodes
Yuto Katsuyama1, Yuta Nakayasu1,2, Hiroaki Kobayashi3
1Research Center of Supercritical Fluid Technology, Tohoku University, Sendai, Miyagi, 980-8579, Japan.
Chemsuschem
|September 17, 2020
Summary
The crystallite interlayer area (Ai) effectively predicts sodium-ion battery anode performance in hard carbons. This new metric correlates strongly with sodium intercalation capacity, offering a superior evaluation method.
Area of Science:
- Materials Science
- Electrochemistry
- Energy Storage
Background:
- Hard carbon (HC) is a leading anode material for sodium-ion batteries.
- Existing performance predictors like Raman intensity ratio, crystallite size (La), and interlayer distance (d002) have limitations.
- These factors do not fully capture the bulk properties relevant to sodium ion (Na+) intercalation.
Purpose of the Study:
- To introduce and validate a new metric, crystallite interlayer area (Ai), for evaluating HC anode performance.
- To demonstrate that Ai, derived from La, d002, and stacking height (Lc), governs Na+ intercalation behavior.
- To establish Ai as a more comprehensive and adaptable evaluation factor compared to conventional methods.
Main Methods:
- Calculation of crystallite interlayer area (Ai) using crystallite lateral size (La), interlayer distance (d002), and stacking height (Lc).
- Analysis of Na+ intercalation capacity (Ci) in various wood-derived hard carbons.
- Correlation analysis between Ai and Ci, and validation against previous studies.
Main Results:
- Wood-derived hard carbons showed similar total capacities (~250 mAh g-1).
- Sodium ion intercalation capacity (Ci) was found to be directly proportional to the crystallite interlayer area (Ai) (R2 = 0.94).
- The Ai metric demonstrated strong correlation with Ci across various hard carbon materials, including those from previous reports.
Conclusions:
- Crystallite interlayer area (Ai) is a critical parameter governing Na+ intercalation in hard carbons.
- Ai provides a more accurate and broadly applicable method for predicting anode performance than traditional metrics.
- This finding facilitates the rational design and selection of advanced hard carbon materials for sodium-ion batteries.
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