Department of Chemical Engineering , Massachusetts Institute of Technology , Cambridge , Massachusetts 02139 , United States.
ACS Nano. 2019 Apr 23;13(4):3940-3952. doi: 10.1021/acsnano.8b07215. Epub 2019 Mar 25.
The rapid and efficient characterization of polydisperse nanoparticle dispersions remains a challenge within nanotechnology and biopharmaceuticals. Current methods for particle sizing, such as dynamic light scattering, analytical ultracentrifugation, and field-flow fractionation, can suffer from a combination of statistical biases, difficult sample preparation, insufficient sampling, and ill-posed data analysis. As an alternative, we introduce a Bayesian method that we call maximum a posteriori nanoparticle tracking analysis (MApNTA) for estimating the size distributions of nanoparticle samples from high-throughput single-particle tracking experiments. We derive unbiased statistical models for two observable quantities in a typical nanoparticle trajectory-the mean square displacement and the trajectory length-as a function of the particle size and calculate size distributions using maximum a posteriori (MAP) estimation with cross validation to mildly regularize solutions. We show that this approach infers nanoparticle size distributions with high resolution by performing extensive Brownian dynamics simulations and experiments with mono- and polydisperse solutions of gold nanoparticles as well as single-walled carbon nanotubes. We further demonstrate particular utility for characterizing minority components and impurity populations and highlight this ability with the identification of an impurity in a commercially produced gold nanoparticle sample. Modern algorithms such as MApNTA should find widespread use in the routine characterization of complex nanoparticle dispersions, allowing for significant advances in nanoparticle synthesis, separation, and functionalization.
多分散纳米粒子分散体的快速和高效表征仍然是纳米技术和生物制药领域的一个挑战。目前用于粒度测量的方法，如动态光散射、分析超速离心和场流分级，可能会受到统计偏差、难以制备样品、采样不足和数据分析不当的综合影响。作为替代方法，我们引入了一种贝叶斯方法，称为最大后验纳米粒子跟踪分析（MApNTA），用于从高通量单粒子跟踪实验中估计纳米粒子样品的粒径分布。我们推导出了典型纳米粒子轨迹中两个可观测量（均方位移和轨迹长度）的无偏统计模型，作为粒子尺寸的函数，并使用交叉验证的最大后验（MAP）估计来计算尺寸分布，以适度正则化解。我们通过对金纳米粒子单分散和多分散溶液以及单壁碳纳米管进行广泛的布朗动力学模拟和实验，证明了这种方法通过高分辨率推断纳米粒子的尺寸分布。我们进一步证明了该方法在表征少数成分和杂质群体方面的特殊用途，并通过鉴定商业生产的金纳米粒子样品中的杂质来突出这种能力。像 MApNTA 这样的现代算法应该在复杂纳米粒子分散体的常规表征中得到广泛应用，从而在纳米粒子合成、分离和功能化方面取得重大进展。
