Polyethylene-based composites containing octaisobutyl polyhedral oligomeric silsesquioxanes obtained by extrusion

Guo, Meng; Demarquette, Nicole R.; David, Éric; Fréchette, M.F.

doi:10.1109/ceidp.2015.7352111

Cited by 1 publication

(1 citation statement)

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“…Transmission electron microscopy (TEM) and SEM are commonly used for imaging nanocomposites. The effective observation range of TEM is less than 1 micron, while sizes of agglomerates published in recent years are generally between 2 and 10 microns [10][11][12]. A small observation scale may increase the randomness and subjectivity of sampling, which leads to higher uncertainty and inconsistency in final experimental results.…”

Section: Introductionmentioning

confidence: 99%

Identification of nanocomposites agglomerates in scanning electron microscopy images based on semantic segmentation

Bai

Wang

Qiang

et al. 2022

IET Nanodielectrics

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The agglomeration causes a significant challenge to nanodielectrics. Identification of agglomerates in scanning electron microscopy (SEM) images is an important step of solving this issue. Motivated by the fast development of image recognition in computer vision, we propose a new approach for agglomerates identification in SEM images of nanodielectrics by semantic segmentation, which is more efficient and accurate than traditional methods. Three models based on convolutional neural networks are investigated in this work, namely pixel blocks classification network, full convolutional segmentation network employed with data augmentation and unsupervised self-encoding network. All three networks can preliminarily identify agglomerates of spherical silicabased blend polyethylene nanocomposites. The mean intersection over union (mIoU) of pixel blocks classification network is 0.843 and it takes 25 s to process an image. Full convolutional segmentation network only needs 0.059 s to process a sample, with a mIoU of 0.777. Unsupervised self-encoding network can reach a mIoU of 0.747 at a speed of 5.806 s. According to the amount of data sets, and requirements for different speed and accuracy, three kinds of networks can be flexibly selected.

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Section: Introductionmentioning

confidence: 99%