Machine-Learning-Driven Synthesis of the Next Generation Carbon Dots with Tunable Fluorescence/Band-Gap

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Inorganic quantum dots exhibit interesting fluorescent and semiconductor properties and are currently explored in biological applications as bioimaging agents or in solar panels/photocatalytic processes, as well as in electronics displays. However, they are based on toxic metals (e.g. Cd, Pb), complicated synthetic processes and exhibit low stability when stored under normal atmospheric conditions. Biomass-derived carbon dots (CDs) have emerged as promising and sustainable candidates to inorganic quantum dots. Yet there remains grand challenges in the field, despite great progress made in the development of novel and sustainable carbon dots, in the engineering of their band gap to fit different requirements for visible light adsorption, as well as producing them with tunable fluorescent properties and high quantum yields. To address these challenges and produce carbon dots with tunable fluorescence and band gap, we propose to correlate reaction parameters in the preparation process of carbon dots to explore structure-properties relationship and potential applications. There is a plethora of experimental data (already existing in Titirici's lab and wider literature) on the synthesis and properties of CDs, so we will apply machine learning (ML) for screening of high-performance materials, first to select diverse conditions under which to synthesise the CDs, and then to weight the importance of synthesis variables and to optimise the desired properties. In this project we will demonstrate how ML-based techniques can offer insights into the successful prediction, optimisation, and acceleration of CDs' synthesis processes and properties leading to emerging applications. A regression ML model on hydrothermally-synthesised CDs from various biomass precursors will be established to reveal the relationship between various synthesis parameters and experimental outcomes as well as enhancing the process-related properties such as the fluorescent quantum yield (QY) and tunable bandgap. The synthesis will first be done in batch, and then ultimately transferred to a flow reactor incorporating advanced process analytical technology (PAT), which will be used to characterise the carbon dots in-situ and tune their properties using computer control, supervised by the ML algorithm.