Laser-induced graphene-based elastomer nanocomposites for soft sensing applications

Jacquin, Tom (2025) Laser-induced graphene-based elastomer nanocomposites for soft sensing applications. PhD thesis, University of Glasgow.

Abstract

Laser-induced graphene (LIG) has gained significant attention as a multifunctional material for flexible and wearable sensor technologies due to its exceptional electrical conductivity, mechanical flexibility, and tunable properties. This thesis presents a comprehensive study of the fabrication, characterization, and application of LIG-polymer nanocomposites, emphasizing the optimization of their electromechanical and thermomechanical performance for soft strain sensing applications.

The fabrication process emphasizes the precise tailoring of LIG properties through controlled laser processing and strategic composite design. In addition to doping LIG with conductive nanoparticles to enhance electrical sensitivity and structural robustness, the integration of LIG with polymer matrices such as PDMS was systematically optimized to modulate the composite’s mechanical flexibility and thermal responsiveness. The effects of laser parameters, substrate interactions, and doping strategies on the microstructure, porosity, and conductivity of the LIG network were thoroughly investigated. This approach enabled the tuning of both electromechanical and thermomechanical behaviour to suit soft sensing applications. Environmental factors, including humidity and temperature, were also considered, as they significantly influence the stability and performance of the composite in real-world conditions.

In addition to experimental advancements, the development of a LabVIEW Python platform for electromechanical testing is highlighted. This platform provided an efficient and precise system for characterizing the strain-resistance behaviour of LIG-polymer composites, enabling the acquisition of high-resolution data critical to this work.

A theoretical model was developed to elucidate the relationship between strain distribution and resistance in LIG-polymer composites. The model incorporates Gaussian strain distributions and integrates experimental resistance-strain data with finite element simulations to quantify the effective resistance under thermal and mechanical loads. Simulations of strain distribution under thermal expansion revealed that temperature-induced narrowing of strain distributions significantly impacts the composite’s resistance response.

Applications of the LIG-polymer composites were explored across diverse fields, including healthcare and robotics, demonstrating the potential of these sensors for wearable health monitoring, soft robotics, and adaptive environmental sensing. Experimental results validated the proposed models and highlighted the influence of laser processing and doping on sensor performance, enabling a deeper understanding of the interplay between material properties and device functionality.

By bridging experimental characterization, theoretical modelling, and practical application, this thesis advances the development of LIG-based strain sensors, providing a foundation for future innovations in soft and wearable electronics.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Additional Information: Supported by funding from the Engineering and Physical Sciences Research Council (EPSRC), and the ASSESSOR project funded by the O ice for Life Sciences (OLS) and The Scottish Government through the SBRI: Overdose detection, response and intervention.
• Subjects: T Technology > TA Engineering (General). Civil engineering (General)
• Colleges/Schools: College of Science and Engineering > School of Engineering
• Funder's Name: Engineering and Physical Sciences Research Council (EPSRC), O ice for Life Sciences (OLS), The Scottish Government
• Supervisor's Name: Amjadi, Dr. Morteza and Heidari, Professor Hadi
• Date of Award: 2025
• Depositing User: Theses Team
• Unique ID: glathesis:2025-85655
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 12 Dec 2025 10:34
• Last Modified: 12 Dec 2025 10:38
• Thesis DOI: 10.5525/gla.thesis.85655
• URI: https://theses.gla.ac.uk/id/eprint/85655

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