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Wheat straw/polypropylene composites are green recycled and biomass-based material. After accelerated aging test of the composite was done, practical and effective methods for characterization and extraction of texture feature of microscopic Scanning Electron Microscopy (SEM) images of composites were investigated in this paper, and involved data c...
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Citations
... Some researchers have adopted machine learning methods to develop prediction models related to material aging. For example, Zhang et al. [141] extracted textural features from SEM images of wheat straw/polypropylene composites, and then recognized and classified the SEM images in different aging periods based on an intelligent classifier. Doblies et al. [142] used an artificial neural network (ANN) and Fourier-transform infrared spectroscopy (FTIR) to predict and quantify the aging time, temperature, and residual strength of an epoxy resin. ...
Aging has a serious impact on the properties of functional polymers. Therefore, it is necessary to study the aging mechanism to prolong the service and storage life of polymer-based devices and materials. Due to the limitations of traditional experimental methods, more and more studies have adopted molecular simulations to analyze the intrinsic mechanisms of aging. In this paper, recent advances in molecular simulations of the aging of polymers and their composites are reviewed. The characteristics and applications of commonly used simulation methods in the study of the aging mechanisms (traditional molecular dynamics simulation, quantum mechanics, and reactive molecular dynamics simulation) are outlined. The current simulation research progress of physical aging, aging under mechanical stress, thermal aging, hydrothermal aging, thermo-oxidative aging, electric aging, aging under high-energy particle impact, and radiation aging is introduced in detail. Finally, the current research status of the aging simulations of polymers and their composites is summarized, and the future development trend has been prospected.
... The morphology of the impact fracture surfaces of the samples was studied by means of a JSM-IT300 LV scanning electron microscopy (Electronics company, Japan). The scanning voltage was 10 KV, and the samples was coated with to eliminate the electron charging effects gold before SEM observation [22]. ...
To investigate the effect of three kinds of colorants on interfacial compatibility between wood and polymer in wood flour/Poly (β-hydroxybutyrate valerate) (PHBV) composites, three colorants are added to color the poplar wood flour/PHBV composites. The effects of the colorant on the interfacial compatibility of the wood plastic composites (WPCs) are studied. The result shows that the mechanical properties of the WPCs tend to increase first and then decrease with increasing colorant. The SEM micrographs of the WPCs show the surface, partially the cracks and grooves of the wood fiber are coated by the dispersed colorant. There are basically no new characteristic peaks in FT-IR spectra, which is proved that three colorants can only enable wood flour to be fully mixed with PHBV. Three colorants can improve the compatibility of wood fiber and PHBV, the mechanical properties of the WPCs, which is significantly increased compared to the WPCs without colorants.
... Since SEM enables to capture fine shapes of biological objects, this technique has been routinely used to collect images suitable for the analysis of textural descriptors [99][100][101][102]. Still, it should be borne in mind that the processing method could affect the texture of a sample. ...
The invention of a scanning electron microscopy (SEM) pushed the imaging methods and allowed for the observation of cell details with a high resolution. Currently, SEM appears as an extremely useful tool to analyse the morphology of biological samples. The aim of this paper is to provide a set of guidelines for using SEM to analyse morphology of prokaryotic and eukaryotic cells, taking as model cases Escherichia coli bacteria and B-35 rat neuroblastoma cells. Herein, we discuss the necessity of a careful sample preparation and provide an optimised protocol that allows to observe the details of cell ultrastructure (≥ 50 nm) with a minimum processing effort. Highlighting the versatility of morphometric descriptors, we present the most informative parameters and couple them with molecular processes. In this way, we indicate the wide range of information that can be collected through SEM imaging of biological materials that makes SEM a convenient screening method to detect cell pathology.
... For instance, the digital image processing technique has been proposed to define the relationship between crack shapes and wearing loss by using SEM images [1]. Texture feature extraction and classification methods have been used for wheat straw/polypropylene composites in order to accelerate the aging test [2]. In another research, the SEM images have been used in an image processing technique to estimate the filler content of polymeric nanocomposites [3]. ...
... According to (2), an image can be divided by r and N(r) that are run on the same property. The r is reduced while the process is continued. ...
This study investigates the use of binary features and fractal dimension analysis to evaluate the dispersion quality of nanofillers in thin polymeric films by using light microscopy images. For this purpose, polymeric films were cast with the inclusion of various montmorillonite (MMT) nanofiller amounts. Then the light microscopy images were captured from the polymeric films then preprocessed for the evaluation. Thresholding process was applied to the obtained images for each nanofiller percentage level. The obtained binary level images were used in the feature extraction process with binary statistics and fractal dimension. Thermogravimetric analysis (TGA) was used to evaluate the flame resist behaviour of polymeric films based on the dispersion quality of nanofillers. The samples with various nanofiller contents were tested using the image processing method and the results were all compared with the TGA results. The results obtained by the feature extraction process and TGA, about the dispersion quality of nanofillers, were all in good agreement.
This article presents an overview of the emerging themes in Autonomous Materials Discovery and Manufacturing (AMDM). This interdisciplinary field is garnering a growing interest among the scientists and engineers in the materials and manufacturing domains as well as those in the Artificial Intelligence (AI) and data sciences domains, and it offers immense research potential for the industrial systems engineering (ISE) and manufacturing fields. Although there are a few reviews related to this topic, they had focused exclusively on sequential experimentation techniques, AI/machine learning applications, or materials synthesis processes. In contrast, this review treats AMDM as a cyberphysical system, comprising an intelligent software brain that incorporates various computational models and sequential experimentation strategies, and a hardware body that integrates equipment platforms for materials synthesis with measurement and testing capabilities. This review offers a balanced perspective of the software and the hardware components of an AMDM system, and discusses the current state-of-the-art and the emerging challenges at the nexus of manufacturing/materials sciences and AI/data sciences in this nascent, exciting area.
