Solvent Dependence of Ionic Liquid-Based Pt Nanoparticle Synthesis: Machine Learning-Aided In-Line Monitoring in a Flow Reactor,ACS Nano

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Solvent Dependence of Ionic Liquid-Based Pt Nanoparticle Synthesis: Machine Learning-Aided In-Line Monitoring in a Flow Reactor
ACS Nano ( IF 15.8 ) Pub Date : 2024-09-05 , DOI: 10.1021/acsnano.4c05807
Bin Pan ₁ , Majed S Madani _{1,

2} , Allison P Forsberg ₃ , Richard L Brutchey ₃ , Noah Malmstadt _{1,

3,

4,

5}

Affiliation

Colloidal platinum nanoparticles (Pt NPs) possess a myriad of technologically relevant applications. A potentially sustainable route to synthesize Pt NPs is via polyol reduction in ionic liquid (IL) solvents; however, the development of this synthetic method is limited by the fact that reaction kinetics have not been investigated. In-line analysis in a flow reactor is an appealing approach to obtain such kinetic data; unfortunately, the optical featurelessness of Pt NPs in the visible spectrum complicates the direct analysis of flow chemistry products via ultraviolet–visible (UV–vis) spectrophotometry. Here, we report a machine learning (ML)-based approach to analyze in-line UV–vis spectrophotometric data to determine Pt NP product concentrations. Using a benchtop flow reactor with ML-interpreted in-line analysis, we were able to investigate NP yield as a function of residence time for two IL solvents: 1-butyl-1-methylpyrrolidinium triflate (BMPYRR-OTf) and 1-butyl-2-methylpyridinium triflate (BMPY-OTf). While these solvents are structurally similar, the polyol reduction shows radically different yields of Pt NPs depending on which solvent is used. The approach presented here will help develop an understanding of how the subtle differences in the molecular structures of these solvents lead to distinct reaction behavior. The accuracy of the ML prediction was validated by particle size analysis and the error was found to be as low as 4%. This approach is generalizable and has the potential to provide information on various reaction outcomes stemming from solvent effects, for example, differential yields, orders of reaction, rate coefficients, NP sizes, etc.

中文翻译：

基于离子液体的 Pt 纳米颗粒合成的溶剂依赖性：流动反应器中机器学习辅助的在线监测

胶体铂纳米颗粒 (Pt NP) 具有多种技术相关应用。合成 Pt NP 的一种潜在可持续途径是通过离子液体 (IL) 溶剂中的多元醇还原；然而，由于反应动力学尚未得到研究，这种合成方法的发展受到限制。连续反应器中的在线分析是获取此类动力学数据的一种有吸引力的方法；不幸的是，Pt NP 在可见光谱中的光学特性使通过紫外-可见 (UV-vis) 分光光度法直接分析流动化学产品变得复杂。在这里，我们报告了一种基于机器学习（ML）的方法来分析在线紫外可见分光光度数据以确定 Pt NP 产品浓度。使用具有 ML 解释在线分析功能的台式流动反应器，我们能够研究 NP 产率与两种 IL 溶剂停留时间的函数关系：1-丁基-1-甲基吡咯烷鎓三氟甲磺酸盐 (BMPYRR-OTf) 和 1-丁基- 2-甲基吡啶鎓三氟甲磺酸盐 (BMPY-OTf)。虽然这些溶剂在结构上相似，但多元醇还原显示出完全不同的 Pt NP 产率，具体取决于所使用的溶剂。这里介绍的方法将有助于了解这些溶剂分子结构的细微差异如何导致不同的反应行为。通过粒度分析验证了 ML 预测的准确性，发现误差低至 4%。这种方法具有普适性，有可能提供有关溶剂效应产生的各种反应结果的信息，例如差异产率、反应级数、速率系数、纳米颗粒大小等。

更新日期：2024-09-05

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