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

无机量子点表现出有趣的荧光和半导体性质，目前正在探索作为生物成像剂或太阳能电池板/光催化过程，以及在电子显示器的生物应用。然而，它们基于有毒金属（例如Cd、Pb）、复杂的合成过程，并且当在正常大气条件下储存时表现出低稳定性。生物质衍生的碳量子点（CD）已成为无机量子点的有前途和可持续的候选者。然而，尽管在开发新型和可持续的碳量子点方面取得了很大进展，但该领域仍然存在巨大的挑战，在设计其带隙以适应可见光吸收的不同要求方面，以及生产具有可调荧光特性和高量子产率的碳量子点。为了解决这些问题，制备荧光和带隙可调的碳量子点，我们提出关联碳量子点制备过程中的反应参数，探索结构-性能关系和潜在应用。关于CD的合成和性质，有大量的实验数据（已经存在于Titirici的实验室和更广泛的文献中），因此我们将应用机器学习（ML）来筛选高性能材料，首先选择合成CD的不同条件，然后权衡合成变量的重要性并优化所需的性质。在这个项目中，我们将展示如何ML为基础的技术可以提供成功的预测，优化和加速CD的合成过程和性能的见解，导致新兴的应用。将建立从各种生物质前体水热合成CD的回归ML模型，以揭示各种合成参数与实验结果之间的关系，以及增强与过程相关的性能，如荧光量子产率（QY）和可调带隙。合成将首先分批进行，然后最终转移到一个采用先进过程分析技术（PAT）的流动反应器中，该技术将用于原位组装碳量子点，并使用计算机控制调整其性能，由ML算法监督。

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.