Development of 2D Material Conformal Coating Technology for Advanced Photonics Devices
Creators: name: Liu, Wenbo
Publisher: name: RMIT University
Year: 2025
Subjects: classificationCode: 340304
subject: Optical properties of materials

description: Integrating 2D materials with optoelectronic devices offers significant potential due to 
their exceptional properties and biocompatibility, paving the way for advanced optical 
fibre sensors. Graphene and its derivatives, mainly graphene oxide (GO) and reduced 
graphene oxide (rGO), stand out for their superior characteristics and scalability. 
Additionally, MXene materials offer exceptional electrical conductivity, high mechanical 
strength, and excellent chemical stability.Integrating MXene and GO coatings with 
optoelectronic devices still faces challenges such as achieving uniform, high-quality 
coatings on various substrates, managing material stability over time, and addressing 
issues with scalability for large-scale production. Achieving strong adhesion and 
compatibility across various device architectures remains challenging. This thesis 
addresses these issues by optimising GO-Poly (diallyldimethylammonium chloride) 
(PDDA) and MXene-PDDA coating processes and incorporating artificial intelligence (AI) 
into fibre Bragg grating (FBG) fabrication. AI-driven automation and femtosecond laser 
precision enhance FBG fabrication quality and reliability, promising advancements in 
telecommunications, energy storage, and intelligent materials. Additionally, this thesis 
explores the integration of GO conformal coating with FBG, laying the groundwork for 
future advancements in combining GO coatings with FBG fabrication techniques. The 
aim is to further enhance sensing performance, improve the durability and stability of 
sensors under diverse environmental conditions, and enable more precise real-time 
detection. Moreover, this research aims to simplify fabrication while preserving high 
sensitivity, thereby broadening the range of applications and enhancing the 
effectiveness and versatility of sensor technologies across various industries. 
This doctoral thesis is structured into four main parts: 
1 Development and characterization of GO coating technology: the research 
optimises GO-PDDA self-assembly processes to enhance coating quality and scalability 
on diverse substrates, including fibres and plastics. GO coatings are characterised by 
mechanical strength and thermal stability, which are crucial for improving FBG 
performance. This research introduces an optimised GO-PDDA self-assembly process 
that significantly enhances the surface smoothness of GO coatings, with 3D
profilometer analysis revealing a 73% improvement in the flatness of 120nm GO films 
prepared using an ultrasonic bath. These advancements result in superior mechanical 
strength and thermal stability, which are essential for enhancing the performance of 
FBGs.
Traditional GO coating methods often need help with consistent quality and limited 
scalability when applied to different materials. The improved GO-PDDA self-assembly 
process addresses these issues by achieving a more uniform and robust coating. This 
enhancement effectively resolves problems related to surface roughness and 
performance variability across diverse substrates, thereby ensuring excellent reliability 
and effectiveness of FBGs in a wide range of applications. The scalability of this coating 
process is demonstrated by its successful application to diverse substrates, including 
optical fibres and plastics, while maintaining high-quality coating performance. 
2 Development and characterization of MXene coating technology: this thesis 
investigates MXene-PDDA self-assembly films, focusing on their properties, such as 
enhanced electrical conductivity and mechanical strength, which are crucial for 
improving FBG applications in energy storage and advanced sensing technologies. 
Additionally, I explore the nonlinear optical absorption (NOA) properties of 2D layered 
MXene films integrated onto Si₃N₄ waveguides. This research advances the development 
of MXene-PDDA self-assembly films by focusing on their exceptional electrical 
conductivity and mechanical strength. Additionally, the study explores the NOA 
properties of 2D layered MXene films integrated onto Si₃N₄ waveguides, providing 
insights into their effectiveness in photonic applications. 
3 Fabrication and characterization of FBGs: the thesis highlights femtosecond laser 
direct writing as a significant advancement in FBG fabrication. It offers precise control 
over grating parameters and enables customised designs for various optical fibre 
applications. It demonstrates the technique's versatility by exploring different functional 
forms like uniform, tilt, gaussian, sinc, and sine functions. 
This research advances femtosecond laser direct writing for fabricating FBGs, offering 
exceptional precision in grating parameter control. The technique customizes FBG 
designs using different functional forms, such as uniform, tilt, gaussian, sinc, and sine 
functions, allowing for tailored optical properties for specific applications. Unlike 
conventional methods, which struggle with precise control and customization, this 
approach provides a more flexible and accurate solution for FBG production. Based on 
the systematic investigation of these apodization functions, the sinc function provides 
the most significant improvement in Side-Lobe Suppression Ratio (SLSR), with an 
enhancement of 16 dB compared to the uniform FBG. The tilt and gaussian functions also 
show substantial improvements of 14.57 dB and 14 dB, respectively. These 
improvements in SLSR lead to better signal quality and higher sensitivity, particularly 
when different coating materials are applied. This research demonstrates that choosing 
the right apodization function not only enhances grating performance but also 
significantly improves the sensitivity of FBGs, making them more effective in optical 
sensing applications.
4 AI-enhanced FBG fabrication: This research introduces an advanced AI model 
integrated into FBG fabrication systems, which automates fibre core recognition and 
positioning. This innovation significantly improves the accuracy and efficiency of FBG 
manufacturing by enabling precise alignment and automated adjustments. 
Traditional FBG fabrication techniques often struggle with manual adjustments and 
alignment, leading to inconsistencies and inefficiencies. For example, expert manual 
alignment requires 1 to 2 minutes per adjustment, while the AI-powered system reduces 
the alignment time to 40-50 seconds. The adoption of AI technology addresses these 
challenges by automating critical aspects of the process, resulting in more accurate and 
consistent fabrication. This approach enhances the quality of FBGs and increases 
production speed and reliability. 
In conclusion, this doctoral thesis significantly advances the fields of GO and MXene 
coating technologies, FBG fabrication, and AI-enhanced manufacturing. By optimizing 
GO-PDDA and MXene-PDDA self-assembly processes, the research improves coating 
quality and scalability, while enhancing mechanical strength, thermal stability, and 
electrical conductivity. Additionally, the thesis explores the integration of GO conformal 
coating with FBG, establishing a foundation for future developments that aim to enhance 
sensing performance. This includes improving the durability and stability of sensors in 
various environmental conditions and enabling more precise real-time detection. 
Furthermore, the research seeks to simplify fabrication processes without sacrificing 
sensitivity, thereby broadening the range of applications and providing more effective, 
versatile sensor technologies for multiple industries. The versatility of femtosecond laser 
direct writing in FBG fabrication is also highlighted, allowing for precise control over 
grating designs tailored for diverse optical applications. Finally, integrating AI into FBG 
fabrication systems transforms production by automating fibre core recognition and 
positioning, leading to enhanced accuracy and efficiency.