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使用 SEMseg 对扫描电子显微镜图像中的纳米棒聚集和形态进行定量分析。

Quantitative Analysis of Nanorod Aggregation and Morphology from Scanning Electron Micrographs Using SEMseg.

机构信息

Department of Electrical and Computer Engineering, Rice University, MS 366, Houston, Texas 77005, United States.

Department of Chemistry, Rice University, MS 60, Houston, Texas 77005, United States.

出版信息

J Phys Chem A. 2020 Jun 25;124(25):5262-5270. doi: 10.1021/acs.jpca.0c03190. Epub 2020 Jun 12.

Abstract

General methods to achieve better physical insight about nanoparticle aggregation and assembly are needed because of the potential role of aggregation in a wide range of materials, environmental, and biological outcomes. Scanning electron microscopy (SEM) is fast and affordable compared to transmission electron microscopy, but SEM micrographs lack contrast and resolution due to lower beam energy, topographic contrast, edge effects, and charging. We present a new segmentation algorithm called SEMseg that is robust to the challenges inherent in SEM micrograph analysis and demonstrate its utility for analyzing gold (Au) nanorod aggregates. SEMseg not only supports nanoparticle size analysis for dispersed nanoparticles, but also discriminates between nanoparticles within an aggregate. We compare our algorithm to those incorporated into the commonly used software ImageJ and demonstrate improved segmentation of aggregate structures. New physical insight about aggregation is demonstrated by the introduction of an order parameter describing side-by-side structure in nanoparticle aggregates. We also present the segmentation and fitting algorithms included in SEMseg within a user-friendly graphical user interface. The resulting code is provided with an open-source interface to provide quantitative image processing tools for researchers to characterize both dispersed nanoparticles and nanoparticle assemblies in SEM micrographs with high throughput.

摘要

由于聚集在广泛的材料、环境和生物学结果中可能发挥的作用,因此需要寻找更好的方法来获得有关纳米颗粒聚集和组装的物理洞察力。与透射电子显微镜相比,扫描电子显微镜(SEM)速度更快、价格更便宜,但由于束能较低、形貌对比、边缘效应和充电,SEM 显微照片的对比度和分辨率较差。我们提出了一种称为 SEMseg 的新分割算法,该算法能够应对 SEM 显微照片分析中固有的挑战,并展示了其在分析金(Au)纳米棒聚集体方面的应用。SEMSeg 不仅支持分散纳米颗粒的纳米颗粒尺寸分析,还可以区分聚集体内的纳米颗粒。我们将我们的算法与常用软件 ImageJ 中包含的算法进行了比较,并证明了它在分割聚集体结构方面的优越性。通过引入一个描述纳米颗粒聚集体中并排结构的序参数,我们展示了关于聚集的新的物理洞察力。我们还在用户友好的图形用户界面中展示了 SEMseg 中包含的分割和拟合算法。我们提供了一个开源接口,为研究人员提供了定量图像处理工具,以便他们能够在 SEM 显微照片中以高通量的方式对分散的纳米颗粒和纳米颗粒组装体进行表征。

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