Work

Deciphering the microstructural complexities of compacted carbon fiber paper through AI-enabled digital twin technology

Year: 2024

Type: article

Abstract: In the decarbonized society based on the renewable sources, the carbon fiber papers (CFPs) are regarded as key porous materials for the electrochemical energy conversion and storage devices. Searching the optimum microstructure of assembled carbon fiber paper under compression is one of the central challenges in this uprising technology. Herein, we present a tomography-based analytical approach to correlate CFP microstructures and transport parameters under the compressed state. For the sake of artificial intelligence, the prediction accuracy on the pore and solid structures is dramatically improved by identifying the true shape of cylindrical carbon fibers. Finally, the structure-property relationship of CFP is thoroughly evaluated over a wide range of the paper thicknesses, PTFE contents and compression ratios. Based on these merits, the relationship between the 3-D microstructure and mass transport has been verified for maximising the efficiency of CFP that is used as an electrode, transport layer, and current collector of next-generation energy devices.

Source: Applied Energy

DOI: 10.1016/j.apenergy.2024.124689