基于深度学习的非球形纳米颗粒布朗运动轨迹分析

Hiroaki Fukuda, Hiromi Kuramochi, Yasushi Shibuta, Takanori Ichiki
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摘要

随着纳米粒子作为有用材料在医疗、制药和工业领域的实际应用,不仅能够评估大小和密度均匀的纳米粒子群,而且能够评估具有丰富多样性的纳米粒子群的技术的重要性正在增加。纳米跟踪分析(NTA)作为一种通过分析布朗运动来测量液体中单个纳米颗粒的大小分布的方法,已被广泛应用于商业应用。我们将深度学习(DL)与NTA相结合,以提取更多的属性信息,并探索了一种方法来实现对单个粒子的评估,以了解它们的多样性。实际的NTA在使用Stokes-Einstein方程量化粒子大小时总是假设为球形,但无法验证被测量的粒子是否真的是球形。为了解决这个问题,我们开发了一个深度学习模型,利用NTA测量获得的BM的时间序列轨迹数据来预测纳米颗粒的形状。结果,我们能够以约80%的准确度区分不同形状的球形和棒状金纳米颗粒,它们被评估为具有几乎相等的粒径,而传统的NTA没有任何区别。此外,我们证明了球形纳米粒子和棒状纳米粒子的混合比例可以从纳米粒子混合样品的测量数据定量估计。这一结果表明,通过将DL分析应用于NTA测量来评估颗粒形状是可能的,这在以前被认为是不可能的,并为进一步增值NTA开辟了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis of Brownian motion trajectories of non-spherical nanoparticles using deep learning
As nanoparticles are being put to practical use as useful materials in the medical, pharmaceutical, and industrial fields, the importance of technologies that can evaluate not only nanoparticle populations of homogeneous size and density but also those of rich diversity is increasing. Nano-tracking analysis (NTA) has been commercialized and widely used as a method to measure individual nanoparticles in liquids and evaluate their size distribution by analyzing Brownian motion. We have combined deep learning (DL) for NTA to extract more property information and explored a methodology to achieve an evaluation for individual particles to understand their diversity. Practical NTA always assumes spherical shape when quantifying particle size using the Stokes–Einstein equation, but it is not possible to verify whether the measured particles are truly spherical. We developed a DL model that predicts the shape of nanoparticles using time series trajectory data of BM obtained from NTA measurements to address this problem. As a result, we were able to discriminate with ∼80% accuracy between spherical and rod-shaped gold nanoparticles of different shapes, which are evaluated to have nearly equal particle size without any discrimination by conventional NTA. Furthermore, we demonstrated that the mixing ratio of spherical and rod-shaped nanoparticles can be quantitatively estimated from measured data of mixed samples of nanoparticles. This result suggests that it is possible to evaluate particle shape by applying DL analysis to NTA measurements, which was previously considered impossible, and opens the way to further value-added NTA.
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