Preparation and Characterisation of Graphene/Chemically Modified Cellulose Composites

Abstract

Cellulose and graphene are two materials that have attracted extensive research due to their unique properties. The abundant -OH and C-H on the cellulose surface can form CH-π and electrostatic interactions with graphene, which make cellulose/graphene composites a promising avenue for enhancing tensile properties, electrical conductivity, and thermal properties. In this project, a graphene/nitrocellulose (NC) composite film was designed and developed via a liquid phase exfoliation technique. By adjusting the content of NC in the composite, a tunable temperature alarm sensor (each 1% rise in NC content leads to a ~1.8 °C increase in response temperature) was further developed that has an ultrafast response and can function under extreme conditions such as underwater or vacuum environments. While integrating graphene/NC composites in a two-dimensional (2D) format has yielded promising results, the transition to a three-dimensional (3D) framework opens up a realm of possibilities. In conventional 2D composites, the interactions between graphene and cellulose primarily occur on a plane, limiting the material's potential for spatial arrangement and structural complexity. Therefore, this project further developed a bacterial cellulose (BC) 3D framework utilising solvent exchange and freeze-drying. This ultralight 3D BC aerogel was assisted by polyethylene glycol (PEG) modification with functionalisation of graphene and metal oxides (MOs) to detect VOC gases. The BC composite with TiO2 has a response time of about 20 s to 1 ppm acetone and limits of detection (LOD) as low as 1.43 ppm at room temperature, while composites with ZnO are more suitable for detecting formaldehyde in indoor air, which becomes effective and reliable strategies for real-time exhaled breath diagnosis of disease and indoor air quality.

Date of Award	1 Aug 2024
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Xiaogang Chen (Supervisor) & Jiashen Li (Supervisor)