Stacey McLellan she/her
Doctoral researcher in Chemistry
Strategic Research Areas
I am a PhD researcher working on the discovery and application of self-assembled organic materials for dynamic imaging and photonic technologies. My work combines experimental materials development with data-driven modelling using KNIME workflows, aiming to uncover structure–property relationships that enable adaptive and sustainable imaging systems. My broader research vision is to bridge chemistry, materials science, engineering and data science to accelerate materials discovery and support the development of next-generation optical and medical imaging devices.
Before joining the CDT, I completed a BSc (Hons) in Chemistry at the University of Glasgow, where I developed a strong interest in combining experimental and computational approaches to materials discovery. During my studies, I completed two competitive research internships that strengthened my interdisciplinary skills. I began as an EPSRC Vacation Intern, where I designed a colorimetric method for detecting transition metals using both laboratory techniques and Density Functional Theory (DFT) calculations to analyse material properties and ligand–metal interactions.
Building on this foundation, my final-year project expanded this work by refining and optimising colour-based detection strategies, with an emphasis on accessibility and efficiency in material sensing. This progression allowed me to deepen my understanding of how computational insights can guide experimental design.
Later, as a Cronin Group Intern, I further advanced my technical skills by collaborating with the U.S. National Institutes of Health (NIH) on medically relevant chemical transformations. In this role, I developed automated experimental workflows covering reaction setup, execution, purification, and product identification. This experience strengthened my expertise in automation, analytical characterisation, and collaborative research, while connecting fundamental chemistry to practical applications.
I believe that scientific innovation thrives in diverse, inclusive, and collaborative environments. My experience working across disciplines has shown me the value of different perspectives in problem-solving and creativity. I am particularly passionate about supporting women in chemistry and related fields, promoting visibility, mentorship, and confidence for the next generation of researchers. I am committed to fostering equality in STEM by encouraging open communication, collaboration, and creating welcoming spaces where everyone can contribute and learn.
Outside the lab, I am a passionate Formula 1 fan, an avid reader (usually accompanied by my cat), and I enjoy playing story-based video games. These interests give me a balance between creativity, strategy, and relaxation — qualities I also value in my approach to research and problem-solving.
Discover and application of self-assembled organics for dynamic imaging technologies
Motivation
The growing demand for high-performance, adaptable imaging and photonic technologies has created an urgent need for materials that can dynamically respond to their environment. Traditional inorganic materials often lack flexibility, sustainability, or tunability. In contrast, self-assembled organic materials offer a versatile platform for designing responsive, lightweight, and sustainable alternatives with controllable optical and electronic properties.
Aims
This project aims to discover and develop new self-assembled organic systems for use in dynamic imaging technologies. The research seeks to understand how molecular design and supramolecular interactions influence material structure and function, and how these relationships can be harnessed to enable responsive optical behaviour. A key objective is to integrate experimental and computational approaches to accelerate the materials discovery process.
Methodology
The project will combine experimental synthesis and characterization of self-assembled organic materials with data-driven modelling and workflow automation using the KNIME platform. By linking structure–property relationships with predictive modelling, the research will identify design principles that guide the creation of next-generation dynamic imaging materials. Collaboration with partners in photonics and materials science will support the translation of discoveries into practical device applications.
Impact
This research will advance the understanding of how molecular self-assembly can be harnessed to engineer responsive materials for imaging and photonic technologies. The outcomes have the potential to impact multiple sectors — from advanced display and sensing systems to medical imaging and diagnostics. The materials developed could enable adaptive optical components, such as tuneable lenses and flexible illumination systems for endoscopic technologies, contributing to safer, smaller, and more efficient medical imaging tools. Additionally, by integrating experimental chemistry with data-driven modelling, this project will establish a sustainable, interdisciplinary framework for materials innovation at the interface of chemistry, materials science, and data science.
My research focuses on the discovery and application of self-assembled organic materials for dynamic imaging and photonic technologies. By integrating experimental materials development with data-driven modelling using KNIME workflows, my goal is to accelerate materials discovery and establish design–property relationships for next-generation imaging systems.
I am keen to collaborate with academic and industrial partners interested in functional organic materials, dynamic or adaptive imaging systems, and data-driven materials research. Collaboration could include co-developing experimental–computational frameworks, exploring new organic semiconductors, or applying self-assembly principles to device design and optimisation.
Potential collaboration areas include:
- Industrial partners in imaging, photonics, display technologies, or materials development.
- Computational scientists working on machine learning or automated workflows for materials modelling.
- Chemists and materials scientists focusing on supramolecular systems or organic electronics.
- Engineers developing photonic or sensing devices.
- Policy-makers and innovation hubs promoting sustainable, data-driven materials innovation.
I welcome opportunities to share expertise and establish interdisciplinary collaborations that bridge molecular design, computation, and device innovation.
