Materials Science and Engineering Program, University of California, Riverside, 900 University Ave., Riverside, California 92521, United States.
Department of Electrical and Computer Engineering, University of California, Riverside, 900 University Ave., Riverside, California 92521, United States.
ACS Appl Mater Interfaces. 2023 Apr 12;15(14):18244-18251. doi: 10.1021/acsami.3c02448. Epub 2023 Apr 3.
The rapid characterization of nanoparticles for morphological information such as size and shape is essential for material synthesis as they are the determining factors for the optical, mechanical, and chemical properties and related applications. In this paper, we report a computational imaging platform to characterize nanoparticle size and morphology under conventional optical microscopy. We established a machine learning model based on a series of images acquired by through-focus scanning optical microscopy (TSOM) on a conventional optical microscope. This model predicts the size of silver nanocubes with an estimation error below 5% on individual particles. At the ensemble level, the estimation error is 1.6% for the averaged size and 0.4 nm for the standard deviation. The method can also identify the tip morphology of silver nanowires from the mix of sharp-tip and blunt-tip samples at an accuracy of 82%. Furthermore, we demonstrated online monitoring for the evolution of the size distribution of nanoparticles during synthesis. This method can be potentially extended to more complicated nanomaterials such as anisotropic and dielectric nanoparticles.
快速对纳米颗粒进行形态特征（如尺寸和形状）的描述对于材料合成至关重要，因为它们是决定纳米颗粒光学、机械和化学性能及相关应用的关键因素。本文报道了一种在常规光学显微镜下对纳米颗粒尺寸和形态进行描述的计算成像平台。我们建立了一个基于常规光学显微镜的通过聚焦扫描光学显微镜（TSOM）获取的一系列图像的机器学习模型。该模型预测银纳米立方体的尺寸，在单个颗粒上的估计误差低于 5%。在整体水平上，平均尺寸的估计误差为 1.6%，标准偏差为 0.4nm。该方法还可以从尖锐和钝形尖端的混合样本中准确识别银纳米线的尖端形态，准确率为 82%。此外，我们还演示了在合成过程中对纳米颗粒尺寸分布演变的在线监测。这种方法可以扩展到更复杂的纳米材料，如各向异性和介电纳米颗粒。
