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Heat capacities and an updated thermodynamic model for the Li-Sn system
Thomas L Reichmann1, Dajian Li, Damian M Cupid
1Karlsruhe Institute of Technology, Institute for Applied Materials-Applied Materials Physics (IAM-AWP), Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany. dajian.li@kit.edu.
This study synthesized pure lithium-tin intermetallic compounds and measured their heat capacities. The new thermodynamic models improve predictions for lithium-ion battery performance.
Area of Science:
- Materials Science
- Thermodynamics
- Electrochemistry
Background:
- Accurate thermodynamic data is crucial for optimizing lithium-ion battery performance.
- Existing thermodynamic models for the lithium-tin system require refinement.
Purpose of the Study:
- Synthesize phase-pure Li17Sn4 and Li7Sn3 intermetallic compounds.
- Measure their heat capacities to develop improved thermodynamic models.
- Enhance the thermodynamic description of the Li-Sn system for better predictive capabilities.
Main Methods:
- Synthesis of intermetallic compounds using controlled heat treatment in tantalum crucibles.
- Characterization via inductively coupled plasma optical emission spectroscopy (ICP-OES) and powder X-ray diffraction (powder-XRD).
- Heat capacity measurements using the step method with a Setaram C80 Tian-Calvet calorimeter.
Main Results:
- Successful synthesis and characterization of phase-pure Li17Sn4 and Li7Sn3.
- Experimentally determined heat capacity data for these compounds.
- Development of restricted Maier-Kelley models and re-optimization of the Li-Sn thermodynamic system.
- Improved agreement between calculated and experimental data for heat capacity, thermodynamics, electrochemistry, and phase diagrams.
Conclusions:
- The re-optimized thermodynamic models provide a more accurate representation of the Li-Sn system.
- These models, based on reliable heat capacity data, can predict electromotive force (emf) values.
- The findings support the development of advanced lithium-ion battery technologies with improved performance predictions.
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