Nanocrystal size distribution analysis from transmission electron microscopy images

Sebille, Martijn van; Maaten, Laurens van der; Xie, Ling; Jarolimek, Karol; Santbergen, Rudi; Swaaij, R.A.C.M.M. van; Leifer, Klaus; Zeman, Miro

doi:10.1039/c5nr06292f

Cited by 8 publications

(4 citation statements)

References 14 publications

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“…[ 10–12 ] The fact that in many practical cases NPs can be approximated to circles has been exploited to detect the NPs either using a Laplacian of Gaussian filter, that is a blob‐detector that responds to circular image structures, or by fitting simple intensity models of the projected shapes to the TEM image. [ 13,14 ] Also the identification of NPs edges using the circular Hough transform is a powerful method that can perform well with images with low‐contrast and it is particularly useful for separating overlapping particles. [ 15,16 ] The main limitation is that these methods are useful for detecting explicitly predefined geometries.…”

Section: Introductionmentioning

confidence: 99%

“…models of the projected shapes to the TEM image. [13,14] Also the identification of NPs edges using the circular Hough transform is a powerful method that can perform well with images with low-contrast and it is particularly useful for separating overlapping particles. [15,16] The main limitation is that these methods are useful for detecting explicitly predefined geometries.…”

Section: Introductionmentioning

confidence: 99%

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TEMAS: A Flexible Non‐AI Algorithm for Metrology of Single‐Core and Core‐Shell Nanoparticles from TEM Images

Noval

Gómez‐Merchán

Leñero‐Bardallo

et al. 2023

Part & Part Syst Charact

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An essential application of electron microscopy is to provide feedback to tune the fabrication of nanoparticles (NPs). Real samples tend to follow a size distribution commonly linked to the synthesis process used and in turn to their functional properties. This study presents an algorithm for measuring particle size distributions in electron microscopy images. State‐of‐the‐art methods based on Artificial Intelligence (e.g., Deep Learning) require extensive datasets of labeled images similar to those expected to be analyzed, and extensive supervised re‐training is often required for cross‐domain application. In contrast, the non‐AI algorithm described in this study is accurate and can be quickly set up for measuring new experimental images in different domains. The accuracy of the method is validated quantitatively and comparing graphical and descriptive statistics. Different size distributions are measured on images of platinum and gold nanocatalysts supported on carbon black, amorphous carbon, and titanium dioxide crystals. Also, images of platinum‐iron core‐shell NPs supported on thin amorphous carbon film are successfully analyzed. The limitation of evaluating different algorithms for NPs metrology is the lack of standards that different researchers can use as ground truth. In order to overcome this limitation, the images and the ground truth measurements presented here are shared as an open dataset.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

TEMAS: A Flexible Non‐AI Algorithm for Metrology of Single‐Core and Core‐Shell Nanoparticles from TEM Images

Noval

Gómez‐Merchán

Leñero‐Bardallo

et al. 2023

Part & Part Syst Charact

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“…In recent years, several studies involving an image analysis methodology applied to TEM images have been reported, − and a few free software packages, including Pebbles and ImageJ, have been made available. Most of these studies have used a single threshold to separate each feature ,,,, or increase the efficiency of the feature counting. , However, these methods may not be suitable for high-throughput analysis because they not only miss many nanoparticles but also are inefficient when identifying images of superimposed or abutted nanoparticles or analyzing TEM images containing uneven shadows caused by uneven transmission of the electron beam through a nanoparticle…”

Section: Introductionmentioning

confidence: 99%

Statistical Characterization of the Morphologies of Nanoparticles through Machine Learning Based Electron Microscopy Image Analysis

et al. 2020

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Although transmission electron microscopy (TEM) may be one of the most efficient techniques available for studying the morphological characteristics of nanoparticles, analyzing them quantitatively in a statistical manner is exceedingly difficult. Herein, we report a method for mass-throughput analysis of the morphologies of nanoparticles by applying a genetic algorithm to an image analysis technique. The proposed method enables the analysis of over 150,000 nanoparticles with a high precision of 99.75% and a low false discovery rate of 0.25%. Furthermore, we clustered nanoparticles with similar morphological shapes into several groups for diverse statistical analyses. We determined that at least 1,500 nanoparticles are necessary to represent the total population of nanoparticles at a 95% credible interval. In addition, the number of TEM measurements and the average number of nanoparticles in each TEM image should be considered to ensure a satisfactory representation of nanoparticles using TEM images. Moreover, the statistical distribution of polydisperse nanoparticles plays a key role in accurately estimating their optical properties. We expect this method to become a powerful tool and aid in expanding nanoparticle-related research into the statistical domain for use in big data analysis.

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“…Nanoparticles are omnipresent in our daily lifes. They can be found in products ranging from cosmetics, textiles, and foods, Several approaches [11][12][13][14][15][16][17][18][19][20][21] have been proposed for automated image analysis of SEM and TEM images. However, most of these approaches rely on single thresholds for the feature separation, [20,21] encounter major difficulties caused by irregular object patterns and noise, [22] or they rely on hand-crafted features for the particle shapes, [14,15] which impair the generalization potential of such algorithms for the characterization of arbitrary nanoparticles or heterogeneous nanoparticle ensembles.…”

Section: Introductionmentioning

confidence: 99%

Synthetic Image Rendering Solves Annotation Problem in Deep Learning Nanoparticle Segmentation

Mill

Wolff²,

Gerrits

et al. 2021

Small Methods

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and are important in various technological fields such as energy, electronics, medicine, and many more. [1][2][3][4][5] However, as a consequence of industrial processes and man-made pollution, unwanted nanoparticle size distributions and concentrations [6] give rise to concerns with respect to human health and environmental pollution. While the nanoparticles' physicochemical properties (size, shape, surface chemistry, etc.) determine the quality of products, [7,8] such characteristics are also important in order to evaluate the biological impact of nanoparticles at a molecular, cellular, and systemic level for any risk assessment for environmental and human health. [9] Characterizing nanoparticles in a dynamic context and on a case-by-case basis, microscopic imaging techniques including those that use focused electron or ion beams in scanning electron microscopes (SEMs) or helium ion microscopes [10] (HIMs) to generate nanometer scale spatial resolution are frequently applied in the scientific community. Given the substantial information content of digital images, these techniques often benefit from, or require, automated high-throughput data analysis that enables the accurate identification of large numbers of particles in a robust way.Nanoparticles occur in various environments as a consequence of man-made processes, which raises concerns about their impact on the environment and human health. To allow for proper risk assessment, a precise and statistically relevant analysis of particle characteristics (such as size, shape, and composition) is required that would greatly benefit from automated image analysis procedures. While deep learning shows impressive results in object detection tasks, its applicability is limited by the amount of representative, experimentally collected and manually annotated training data. Here, an elegant, flexible, and versatile method to bypass this costly and tedious data acquisition process is presented. It shows that using a rendering software allows to generate realistic, synthetic training data to train a state-of-the art deep neural network. Using this approach, a segmentation accuracy can be derived that is comparable to man-made annotations for toxicologically relevant metal-oxide nanoparticle ensembles which were chosen as examples. The presented study paves the way toward the use of deep learning for automated, highthroughput particle detection in a variety of imaging techniques such as in microscopies and spectroscopies, for a wide range of applications, including the detection of micro-and nanoplastic particles in water and tissue samples.

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Nanocrystal size distribution analysis from transmission electron microscopy images

Cited by 8 publications

References 14 publications

TEMAS: A Flexible Non‐AI Algorithm for Metrology of Single‐Core and Core‐Shell Nanoparticles from TEM Images

TEMAS: A Flexible Non‐AI Algorithm for Metrology of Single‐Core and Core‐Shell Nanoparticles from TEM Images

Statistical Characterization of the Morphologies of Nanoparticles through Machine Learning Based Electron Microscopy Image Analysis

Synthetic Image Rendering Solves Annotation Problem in Deep Learning Nanoparticle Segmentation

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