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Mass and Charge Transport in Li 1-δ CoO 2 Thin Films-A Complete Set of Properties and Its Defect Chemical Interpretation.

Andreas Ewald BumbergerClaudia SteinbachJoseph RingJuergen Fleig
Published in: Chemistry of materials : a publication of the American Chemical Society (2022)
Lithium insertion materials are an essential class of mixed ionic and electronic conductors, and their electrochemical properties depend on the resistive and capacitive interplay of ions and electrons. However, complete sets of the corresponding elementary material parameters, that is, composition-dependent ionic and electronic conductivity, chemical capacitance, and charge-transfer resistance, are rarely reported for lithium-ion battery electrode materials. Moreover, the interpretation of these properties from a defect chemical point of view is not very common. In this work, the impedance of sputtered Li 1-δ CoO 2 thin films is analyzed to extract the fundamental electrochemical properties as a function of state-of-charge (SOC). Within the accessible SOC range, the charge transfer resistance and ionic conductivity vary by more than 1 order of magnitude. The chemical capacitance determined from impedance spectra agrees excellently with the differential capacitance from charge/discharge curves, and, in the dilute regime, even matches the absolute values predicted by defect thermodynamics. The evolution of lithium diffusivity along the charge curve is deconvoluted into the separate contributions of ionic conductivity and chemical capacitance. Finally, we apply the principles of defect chemistry to evaluate the observed trends in terms of lithium activity and point defect concentrations and provide a tentative defect model that is consistent with our results. The consistency of impedance measurements, cycling data, and thermodynamic theory highlights the key role of the chemical capacitance as a powerful material descriptor and emphasizes the relevance of defect chemical concepts for all lithium insertion electrode materials.
Keyphrases
  • solid state
  • ionic liquid
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  • machine learning
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  • artificial intelligence
  • data analysis