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

机器学习驱动的具有可调荧光/带隙的下一代碳点合成

• 批准号: 2754236
• 金额: --
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2754236
• 关键词: Machine; Learning; Driven; Synthesis; Next

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.

无机量子点表现出有趣的荧光和半导体性质，目前在生物成像、太阳能电池板/光催化过程以及电子显示器中的应用被探索。然而，它们以有毒金属(如Cd、Pb)为基础，合成过程复杂，在正常大气条件下储存时稳定性较低。生物质衍生碳点(Cd)是一种很有前途的、可持续的无机量子点候选材料。然而，尽管在开发新型和可持续的碳点方面取得了很大进展，在设计它们的带隙以适应可见光吸收的不同要求，以及生产具有可调荧光特性和高量子产率的它们方面，该领域仍然存在巨大的挑战。为了应对这些挑战，并制备出具有可调荧光和带隙的碳点，我们建议在碳点的制备过程中关联反应参数，以探索结构与性质的关系和潜在的应用。有太多的实验数据(在Titirici的实验室和更广泛的文献中已经存在)关于CD的合成和性能，所以我们将应用机器学习(ML)来筛选高性能材料，首先选择不同的条件来合成CDS，然后权衡合成变量的重要性并优化所需的性能。在这个项目中，我们将展示基于ML的技术如何提供对CDS合成过程和性能的成功预测、优化和加速的见解，从而导致新兴的应用。建立了不同生物质前驱体水热合成硫化镉的回归ML模型，以揭示各种合成参数与实验结果之间的关系，并提高了与过程相关的性质，如荧光量子产率(QY)和可调带隙。合成将首先批量完成，然后最终转移到采用先进过程分析技术(PAT)的流动反应器，该反应器将用于现场表征碳点，并在ML算法的监督下通过计算机控制调整其性质。