• Korean Chemical Engineering Research
• /
• v.61 no.1
• /
• pp.32-38
• /
• 2023

In this study, the electrochemical properties of dopamine coated silicon/silicon carbide/carbon(Si/SiC/C) composite materials were investigated to improve cycle stability and rate performance of silicon-based anode active material for lithium-ion batteries. After synthesizing CTAB/SiO₂ using the Stöber method, the Si/SiC composites were prepared through the magnesium thermal reduction method with NaCl as heat absorbent. Then, carbon coated Si/SiC anode materials were synthesized through polymerization of dopamine. The physical properties of the prepared Si/SiC/C anode materials were analyzed by SEM, TEM, XRD and BET. Also the electrochemical performance were investigated by cycle stability, rate performance, cyclic voltammetry and EIS test of lithium-ion batteries in 1 M LiPF₆ (EC: DEC = 1:1 vol%) electrolyte. The prepared 1-Si/SiC showed a discharge capacity of 633 mAh/g and 1-Si/SiC/C had a discharge capacity of 877 mAh/g at 0.1 C after 100 cycles. Therefore, it was confirmed that cycle stability was improved through dopamine coating. In addition, the anode materials were obtain a high capacity of 576 mAh/g at 5 C and a capacity recovery of 99.9% at 0.1 C/0.1 C.

• Composites Research
• /
• v.36 no.3
• /
• pp.205-210
• /
• 2023

In this study, we developed a transfer learning framework based on homogenization data for efficient prediction of the effective mechanical properties and thermal conductivity of cellular foam structures. Mean-field homogenization (MFH) based on the Eshelby's tensor allows for efficient prediction of properties in porous structures including ellipsoidal inclusions, but accurately predicting the properties of cellular foam structures is challenging. On the other hand, finite element homogenization (FEH) is more accurate but comes with relatively high computational cost. In this paper, we propose a data-driven transfer learning framework that combines the advantages of mean-field homogenization and finite element homogenization. Specifically, we generate a large amount of mean-field homogenization data to build a pre-trained model, and then fine-tune it using a relatively small amount of finite element homogenization data. Numerical examples were conducted to validate the proposed framework and verify the accuracy of the analysis. The results of this study are expected to be applicable to the analysis of materials with various foam structures.