Machine Learning to Reveal Nanoparticle Dynamics from Liquid-Phase TEM Videos
Machine Learning to Reveal Nanoparticle Dynamics from Liquid-Phase TEM Videos
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DOI：
10.1021/acscentsci.0c00430
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发表时间：
2020-07
影响因子：
18.2
通讯作者：
Lehan Yao;Zihao Ou;Binbin Luo;Cong Xu;Qian Chen
中科院分区：
文献类型：
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作者：
Lehan Yao;Zihao Ou;Binbin Luo;Cong Xu;Qian Chen
液相透射电子显微镜（TEM）最近已被应用于材料化学，以获得在纳米分辨率的各种反应和相变动力学的基本理解。然而，从液相TEM视频中定量提取物理和化学参数仍然受到缺乏与视频的高噪声和空间异质性兼容的自动分析方法的阻碍。在这里，我们首次将液相TEM成像与我们基于称为U-Net神经网络的机器学习模型的定制分析框架相结合。我们的工作流程使这种组合成为可能，以生成模拟TEM图像作为具有明确定义的地面真实值的训练数据。我们将这个框架应用到三个典型的胶体纳米颗粒系统，涉及它们的扩散和相互作用，反应动力学和组装动力学，所有这些都通过液相TEM实时和实时空间解决。不同形状的各向异性纳米粒子的属性的多样性进行了映射，包括纳米棱镜的各向异性相互作用景观，曲率依赖和分阶段的蚀刻轮廓的纳米棒，和一个意想不到的动力学规律的一阶链式组装凹纳米立方体。这些代表纳米级性质的系统在实验上是无法实现的。与流行的图像分割方法相比，U-Net显示出上级的能力，可以从高噪声和波动的背景中预测纳米颗粒的位置和形状边界，这是液相TEM视频中常见的，有时是不可避免的挑战。我们希望我们的框架将液相TEM的效力推到其完全定量水平，并以高通量和统计学显著的方式对合成和生物纳米材料的纳米级动力学进行深入研究。
Liquid-phase transmission electron microscopy (TEM) has been recently applied to materials chemistry to gain fundamental understanding of various reaction and phase transition dynamics at nanometer resolution. However, quantitative extraction of physical and chemical parameters from the liquid-phase TEM videos remains bottlenecked by the lack of automated analysis methods compatible with the videos’ high noisiness and spatial heterogeneity. Here, we integrate, for the first time, liquid-phase TEM imaging with our customized analysis framework based on a machine learning model called U-Net neural network. This combination is made possible by our workflow to generate simulated TEM images as the training data with well-defined ground truth. We apply this framework to three typical systems of colloidal nanoparticles, concerning their diffusion and interaction, reaction kinetics, and assembly dynamics, all resolved in real-time and real-space by liquid-phase TEM. A diversity of properties for differently shaped anisotropic nanoparticles are mapped, including the anisotropic interaction landscape of nanoprisms, curvature-dependent and staged etching profiles of nanorods, and an unexpected kinetic law of first-order chaining assembly of concave nanocubes. These systems representing properties at the nanoscale are otherwise experimentally inaccessible. Compared to the prevalent image segmentation methods, U-Net shows a superior capability to predict the position and shape boundary of nanoparticles from highly noisy and fluctuating background—a challenge common and sometimes inevitable in liquid-phase TEM videos. We expect our framework to push the potency of liquid-phase TEM to its full quantitative level and to shed insights, in high-throughput and statistically significant fashion, on the nanoscale dynamics of synthetic and biological nanomaterials.