Many applications of inorganic nanoparticles (NPs), including photocatalysis, photovoltaics, chemical and biochemical sensing, and theranostics, are governed by NP optical properties. Exploration and identification of reaction conditions for the synthesis of NPs with targeted spectroscopic characteristics is a time-, labor-, and resource-intensive task, as it involves the optimization of multiple interdependent reaction conditions. Integration of machine learning (ML) and microfluidics (MF) offers accelerated identification and optimization of reaction conditions for NP synthesis. Here, an autonomous ML-driven, oscillatory MF platform for the synthesis of NPs is reported. The platform utilized multiple recipes and reaction times for the synthesis of NPs with different dimensions, conducted spectroscopic NP characterization, and employed ML approaches to analyze multiple yet prioritized spectroscopic NP characteristics, and identified reaction conditions for the synthesis of NPs with targeted optical properties. The platform is also used to develop an understanding of the relationship between reaction conditions and NP properties. This study shows the strong potential of ML-driven oscillatory MF platforms in materials science and paves the way for automated NP development.

中文翻译：

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金属纳米粒子合成自驱动平台：结合微流体和机器学习

无机纳米粒子 (NP) 的许多应用，包括光催化、光伏、化学和生化传感以及治疗诊断学，都受 NP 光学特性的控制。探索和鉴定用于合成具有靶向光谱特征的 NPs 的反应条件是一项时间、劳动力和资源密集型任务，因为它涉及多个相互依赖的反应条件的优化。机器学习 (ML) 和微流体 (MF) 的集成提供了 NP 合成反应条件的加速识别和优化。在这里，报告了用于合成 NP 的自主 ML 驱动的振荡 MF 平台。该平台利用多种配方和反应时间来合成不同尺寸的纳米颗粒，进行光谱纳米颗粒表征，并采用 ML 方法来分析多个但优先考虑的光谱 NP 特征，并确定合成具有目标光学特性的 NP 的反应条件。该平台还用于了解反应条件和 NP 性质之间的关系。这项研究显示了 ML 驱动的振荡 MF 平台在材料科学中的强大潜力，并为自动化 NP 开发铺平了道路。