Fabrication of porous carbon nanofibres from electrospun fibre templates

Abstract

This thesis demonstrates three studies based on the easily accessible and low-cost production methods of porous carbon nanofibres (PCNFs). The method in the first study is the poor solvent induced method. Electrospun polyacrylonitrile (PAN)/ polyvinylpyrrolidone (PVP) fibres with a porous structure were formed by the induced crystallisation and PVP dissolving using a poor solvent, which is a mixture of solvent (DMF) and non-solvent (H2O). PCNFs with enhanced surface area and micropore volume were achieved after heating and they were then incorporated with cellulose fibres for humidity sensing. The dynamic and static humidity sensing measurements both show that PCNFs have a good potential to serve as a sensitive resistance-based sensor. In the second study, selective removal method was employed. A cheap sublimation agent terephthalic acid (TPA) was mixed with PAN/PVP for electrospinning. The porous fibre structure was generated by the TPA sublimation during high-temperature heating treatment. The effects of PVP with varying molecular weights on the structures and properties of resulting PCNFs were investigated. It was found that the pore structure is able to be tailored by altering the molecular weight of PVP and consequently mechanical and electrical properties of PCNFs can be affected. Selective removal and non-solvent induced methods were used jointly to prepare PCNFs in the third study. A high humid environment was created for PAN/PVP/TPA electrospinning and phase separation was induced by large amounts of water vapour (non-solvent). Highly porous carbon nanofibres (HPCNFs) were successfully fabricated after TPA sublimation. The surface area and pore volume of HPCNFs are almost three times higher than that of PCNFs (which were electrospun under normal humidity environment). Despite the lower conductivity, HPCNFs have shown better EMI shielding performance than PCNFs, displaying a competitive specific total shielding effectiveness of 537.8 dB/mm.

Date of Award	1 Aug 2023
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Xiaogang Chen (Supervisor) & Jiashen Li (Supervisor)