Intelligent nanoscope for rapid nanomaterial identification and classification.
Intelligent nanoscope for rapid nanomaterial identification and classification.
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DOI：
10.1039/d2lc00206j
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发表时间：
2022-08-09
期刊：
影响因子：
6.1
通讯作者：
Huang, Tony Jun
中科院分区：
文献类型：
--
作者：
Jin, Geonsoo;Hong, Seongwoo;Rich, Joseph;Xia, Jianping;Kim, Kyeri;You, Lingchong;Zhao, Chenglong;Huang, Tony Jun
颗粒和材料的机器学习图像识别和分类是一个快速发展的领域。然而，纳米材料的识别和分类取决于图像分辨率、图像视野和处理时间。光学显微镜是世界各地实验室中最广泛使用的技术之一，因为它们具有识别和分类关键微米尺寸物体和过程的非破坏性能力，但是由于光学器件的衍射极限和小视场，用传统显微镜识别和分类关键纳米尺寸物体和过程超出了其能力范围。为了克服纳米材料识别和分类的这些挑战，我们开发了一种智能纳米显微镜，该智能纳米显微镜结合了机器学习和基于微球阵列的成像，以：（1）通过微球成像超过显微镜物镜的衍射极限，以提供高分辨率图像;（2）通过利用微球阵列提供大视场成像，而不牺牲分辨率;（3）通过微球阵列提供大视场成像，而不牺牲分辨率。以及（3）使用深度卷积神经网络对纳米材料进行快速分类。智能纳米显微镜从单个图像帧中提供超过46个放大图像，因此我们在2秒内收集了1，000多个图像。此外，智能纳米显微镜使用1，000张训练集图像实现了95%的纳米材料分类准确率，比没有微球阵列的准确率高45%。智能纳米显微镜还使用50，000张训练集图像实现了92%的细菌分类准确率，比没有微球阵列的准确率高35%。该平台实现了对尺寸差异极小的纳米材料的快速、准确检测和分类。该设备的能力有可能进一步检测和分类较小的生物纳米材料，如病毒或细胞外囊泡。
Machine learning image recognition and classification of particles and materials is a rapidly expanding field. However, nanomaterial identification and classification are dependent on the image resolution, the image field of view, and the processing time. Optical microscopes are one of the most widely utilized technologies in laboratories across the world, due to their nondestructive abilities to identify and classify critical micro-sized objects and processes, but identifying and classifying critical nano-sized objects and processes with a conventional microscope are outside of its capabilities, due to the diffraction limit of the optics and small field of view. To overcome these challenges of nanomaterial identification and classification, we developed an intelligent nanoscope that combines machine learning and microsphere array-based imaging to: (1) surpass the diffraction limit of the microscope objective with microsphere imaging to provide high-resolution images; (2) provide large field-of-view imaging without the sacrifice of resolution by utilizing a microsphere array; and (3) rapidly classify nanomaterials using a deep convolution neural network. The intelligent nanoscope delivers more than 46 magnified images from a single image frame so that we collected more than 1,000 images within 2 seconds. Moreover, the intelligent nanoscope achieves a 95% nanomaterial classification accuracy using 1,000 images of training sets, which is 45% more accurate than without the microsphere array. The intelligent nanoscope also achieves a 92% bacteria classification accuracy using 50,000 images of training sets, which is 35% more accurate than without the microsphere array. This platform accomplished rapid, accurate detection and classification of nanomaterials with miniscule size differences. The capabilities of this device wield the potential to further detect and classify smaller biological nanomaterial, such as viruses or extracellular vesicles.
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DOI：
10.1111/j.1365-2818.1989.tb02890.x
发表时间：
1989-09-01
期刊：
JOURNAL OF MICROSCOPY-OXFORD
影响因子：
--
作者：
BENNETT, JA
通讯作者：
BENNETT, JA
影响因子：
10.6
作者：
Chan, Tsung-Han;Jia, Kui;Ma, Yi
通讯作者：
Ma, Yi
影响因子：
5.8
作者：
Chen, Lianwei;Zhou, Yan;Hong, Minghui
通讯作者：
Hong, Minghui
影响因子：
17.1
作者：
Chaturvedi, Pratik;Hsu, Keng H.;Fang, Nicholas X.
通讯作者：
Fang, Nicholas X.
DOI：
10.1073/pnas.1904770116
发表时间：
2019-09-24
影响因子：
11.1
作者：
Canady, Taylor D.;Li, Nantao;Cunningham, Brian T.
通讯作者：
Cunningham, Brian T.