Single-Nanoparticle Orientation Sensing by Deep Learning,ACS Central Science

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Single-Nanoparticle Orientation Sensing by Deep Learning
ACS Central Science ( IF 12.7 ) Pub Date : 2020-11-09 , DOI: 10.1021/acscentsci.0c01252
Jingtian Hu ₁ , Tingting Liu ₁ , Priscilla Choo ₁ , Shengjie Wang ₂ , Thaddeus Reese ₃ , Alexander D. Sample ₁ , Teri W. Odom _{1,

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Affiliation

This paper describes a computational imaging platform to determine the orientation of anisotropic optical probes under differential interference contrast (DIC) microscopy. We established a deep-learning model based on data sets of DIC images collected from metal nanoparticle optical probes at different orientations. This model predicted the in-plane angle of gold nanorods with an error below 20°, the inherent limit of the DIC method. Using low-symmetry gold nanostars as optical probes, we demonstrated the detection of in-plane particle orientation in the full 0–360° range. We also showed that orientation predictions of the same particle were consistent even with variations in the imaging background. Finally, the deep-learning model was extended to enable simultaneous prediction of in-plane and out-of-plane rotation angles for a multibranched nanostar by concurrent analysis of DIC images measured at multiple wavelengths.

中文翻译：

深度学习的单纳米粒子定向传感

本文介绍了一种计算成像平台，用于在差分干涉对比（DIC）显微镜下确定各向异性光学探头的方向。我们基于从不同方向的金属纳米粒子光学探针收集的DIC图像的数据集建立了深度学习模型。该模型预测了金纳米棒的面内角度，其误差低于20°，这是DIC方法的固有极限。使用低对称金纳米星作为光学探针，我们展示了在0-360°整个范围内检测面内粒子取向的方法。我们还表明，即使成像背景发生变化，同一粒子的取向预测也是一致的。最后，

更新日期：2020-12-23

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