Han Yu, Tang Bijun, Wang Liang, Bao Hong, Lu Yuhao, Guan Cuntai, Zhang Liang, Le Mengying, Liu Zheng, Wu Minghong
Institute of Nanochemistry and Nanobiology, School of Environmental and Chemical Engineering, Shanghai University, 99 Shangda Road, BaoShan District, Shanghai 200444, P.R. China.
School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore.
ACS Nano. 2020 Nov 24;14(11):14761-14768. doi: 10.1021/acsnano.0c01899. Epub 2020 Oct 26.
Knowing the correlation of reaction parameters in the preparation process of carbon dots (CDs) is essential for optimizing the synthesis strategy, exploring exotic properties, and exploiting potential applications. However, the integrated screening experimental data on the synthesis of CDs are huge and noisy. Machine learning (ML) has recently been successfully used for the screening of high-performance materials. Here, we demonstrate how ML-based techniques can offer insight into the successful prediction, optimization, and acceleration of CDs' synthesis process. A regression ML model on hydrothermal-synthesized CDs is established capable of revealing the relationship between various synthesis parameters and experimental outcomes as well as enhancing the process-related properties such as the fluorescent quantum yield (QY). CDs exhibiting a strong green emission with QY up to 39.3% are obtained through the combined ML guidance and experimental verification. The mass of precursors and the volume of alkaline catalysts are identified as the most important features in the synthesis of high-QY CDs by the trained ML model. The CDs are applied as an ultrasensitive fluorescence probe for monitoring the Fe ion because of their superior optical behaviors. The probe exhibits the linear response to the Fe ion with a wide concentration range (0-150 μM), and its detection limit is 0.039 μM. Our findings demonstrate the great capability of ML to guide the synthesis of high-quality CDs, accelerating the development of intelligent material.
了解碳点（CDs）制备过程中反应参数的相关性对于优化合成策略、探索奇异性质和开发潜在应用至关重要。然而，关于CDs合成的综合筛选实验数据庞大且有噪声。机器学习（ML）最近已成功用于高性能材料的筛选。在此，我们展示了基于ML的技术如何能够深入了解CDs合成过程的成功预测、优化和加速。建立了一个关于水热合成CDs的回归ML模型，该模型能够揭示各种合成参数与实验结果之间的关系，并提高与过程相关的性质，如荧光量子产率（QY）。通过ML指导和实验验证相结合，获得了QY高达39.3%的强绿色发射CDs。经训练的ML模型确定前驱体质量和碱性催化剂体积是合成高QY CDs中最重要的特征。由于其优异的光学性能，这些CDs被用作监测铁离子的超灵敏荧光探针。该探针对铁离子在宽浓度范围（0 - 150 μM）内表现出线性响应，其检测限为0.039 μM。我们的研究结果证明了ML在指导高质量CDs合成、加速智能材料开发方面的强大能力。
