Williamson Emily M, Tappan Bryce A, Mora-Tamez Lucía, Barim Gözde, Brutchey Richard L
Department of Chemistry, University of Southern California, Los Angeles, California 90089, United States.
ACS Nano. 2021 Jun 22;15(6):9422-9433. doi: 10.1021/acsnano.1c00502. Epub 2021 Apr 20.
Thiospinels, such as CoNiS, are showing promise for numerous applications, including as catalysts for the hydrogen evolution reaction, hydrodesulfurization, and oxygen evolution and reduction reactions; however, CoNiS has not been synthesized as small, colloidal nanocrystals with high surface-area-to-volume ratios. Traditional optimization methods to control nanocrystal attributes such as size typically rely upon one variable at a time (OVAT) methods that are not only time and labor intensive but also lack the ability to identify higher-order interactions between experimental variables that affect target outcomes. Herein, we demonstrate that a statistical design of experiments (DoE) approach can optimize the synthesis of CoNiS nanocrystals, allowing for control over the responses of nanocrystal size, size distribution, and isolated yield. After implementing a 2 fractional factorial design, the statistical screening of five different experimental variables identified temperature, Co:Ni precursor ratio, Co:thiol ratio, and their higher-order interactions as the most critical factors in influencing the aforementioned responses. Second-order design with a Doehlert matrix yielded polynomial functions used to predict the reaction parameters needed to individually optimize all three responses. A multiobjective optimization, allowing for the simultaneous optimization of size, size distribution, and isolated yield, predicted the synthetic conditions needed to achieve a minimum nanocrystal size of 6.1 nm, a minimum polydispersity (σ/) of 10%, and a maximum isolated yield of 99%, with a desirability of 96%. The resulting model was experimentally verified by performing reactions under the specified conditions. Our work illustrates the advantage of multivariate experimental design as a powerful tool for accelerating control and optimization in nanocrystal syntheses.
硫族尖晶石，如CoNiS，在众多应用中展现出了潜力，包括作为析氢反应、加氢脱硫以及析氧和氧还原反应的催化剂；然而，CoNiS尚未被合成出具有高表面积与体积比的小尺寸胶体纳米晶体。传统的控制纳米晶体属性（如尺寸）的优化方法通常依赖于一次只改变一个变量（OVAT）的方法，这种方法不仅耗时费力，而且缺乏识别影响目标结果的实验变量之间高阶相互作用的能力。在此，我们证明实验设计（DoE）方法可以优化CoNiS纳米晶体的合成，从而控制纳米晶体尺寸、尺寸分布和分离产率等响应。在实施二分之一分式析因设计后，对五个不同实验变量进行统计筛选，确定温度、Co:Ni前驱体比例、Co:硫醇比例及其高阶相互作用是影响上述响应的最关键因素。使用Doehlert矩阵进行二阶设计得到了多项式函数，用于预测分别优化所有三个响应所需的反应参数。多目标优化允许同时优化尺寸、尺寸分布和分离产率，预测出实现最小纳米晶体尺寸为6.1 nm、最小多分散性（σ/）为10%以及最大分离产率为99%所需的合成条件，合意度为96%。通过在指定条件下进行反应，对所得模型进行了实验验证。我们的工作说明了多变量实验设计作为加速纳米晶体合成控制和优化的有力工具的优势。
