Analysis of the dispersion state of pitch particles in polymers for nanofiber fabrication by optical microscopy and image processing

Oshida, Kyoichi; Murata, Masahiko; Takizawa, Yukio

doi:10.1016/j.jpcs.2022.110585

Cited by 7 publications

(3 citation statements)

References 29 publications

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“…To examine the load displacement relationship for a single polymer nanofiber, microelectromechanical systems equipment is designed with on-chip leaf-spring load cell and actuators with grip for fiber specimen mounting [ 119 , 120 , 121 ]. High manipulation accuracy is necessary to position the nanofiber between grasping components while being observed through an optical microscope [ 114 , 122 ] or scanning electron microscope [ 123 ]. Although the MEMS force sensor seems to be precise, using it requires in situ SEM or necessitates that the sensor is glued to fiber after being installed onto a MEMS testing stand [ 124 ].…”

Section: Properties Of Polymer Nanofibersmentioning

confidence: 99%

Electrospun Polymer Nanofibers: Processing, Properties, and Applications

2022

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Electrospun polymer nanofibers (EPNF) constitute one of the most important nanomaterials with diverse applications. An overall review of EPNF is presented here, starting with an introduction to the most attractive features of these materials, which include the high aspect ratio and area to volume ratio as well as excellent processability through various production techniques. A review of these techniques is featured with a focus on electrospinning, which is the most widely used, with a detailed description and different types of the process. Polymers used in electrospinning are also reviewed with the solvent effect highlighted, followed by a discussion of the parameters of the electrospinning process. The mechanical properties of EPNF are discussed in detail with a focus on tests and techniques used for determining them, followed by a section for other properties including electrical, chemical, and optical properties. The final section is dedicated to the most important applications for EPNF, which constitute the driver for the relentless pursuit of their continuous development and improvement. These applications include biomedical application such as tissue engineering, wound healing and dressing, and drug delivery systems. In addition, sensors and biosensors applications, air filtration, defense applications, and energy devices are reviewed. A brief conclusion is presented at the end with the most important findings and directions for future research.

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Section: Properties Of Polymer Nanofibersmentioning

confidence: 99%

Electrospun Polymer Nanofibers: Processing, Properties, and Applications

2022

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“…The pipeline includes: annotation with the aid of using cycle-regular generative antagonistic networks (GANs), extraction of salient photo patches, category of the extracted patches, and finally, regression to yield the very last microorganism matter. Oshida et al, (2022) postulated that, to examine the spatial frequencies of the particle distribution, twodimensional rapid Fourier transform (2D-FFT) has been carried out to the cross-sectional image data.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning and Computer Vision: Machine Learning Analysis and Image Processing of Puromycin Treated Microscopy

Owoeye¹,

Ojo-Omoniyi²

2022

Int.J.Curr.Microbiol.App.Sci

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Digital image processing involves the usage of a functional algorithm to process images with special regions of interest. In most case scenarios, this is termed as an active aspect of digital signal processing; image processing comes with several rewards over analog image processing. Its relevance and application spans Autonomous Vehicles, Biometric fingerprint technologies as well as Face recognition applications. Reliable statistics through feature engineering from the image can be extracted and in turn serve as focus points of deep learning insights. Besides, its application in monitoring Climatic changes, Agricultural crop yields, security measures, industrial manufacturing as well as medical fields exponentially advances each day. Meanwhile, deep learning being a feature of Artificial Intelligence has brought forward several useful models that is being used as transfer base for further model accuracies and baselines. In this study, we make use of a certain Microscopy datasets, sampling one of the images for digital processing, in order to gain useful insights through Cropped Quantizing, Laplace Edge Detection and Gaussian noise with sigma methods respectively. The statistical results of the extracted image features through Support Vector Method (SVM) give accuracy of up to 75%.

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“…Generally, pitch plays a significant role in CNFs in not only enhancing degree of graphitization partially but also building a conductive network with its liquid-crystalline structure, thus improving the electrical conductivity of CNFs for supercapacitor electrodes. 5,6 Tian et al 7 fabricated a freestanding and binder-free CNFs mats with a cross-linked structure by Jialing Zou and Mengzhu Li authors contributed equally to this work. using pitch as a precursor.…”

Section: Introductionmentioning

confidence: 99%

Effect of adding lignin on preparation and properties of pitch/PAN‐derived electrospun nanofibers inducing cross‐linking structure

Zou,

Li,

Zhang

et al. 2024

J of Applied Polymer Sci

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In this work, the content of lignin, pitch, and PAN as well as the preparation process would be systematically controlled in order to prepare carbon nanofibers (CNFs) with cross‐linked structure, high specific surface area, and good flexibility. The evolution of nanofibers with different lignin contents during stabilization and carbonization were explored, and the properties of the resultant CNFs as supercapacitor self‐supported electrodes were compared. The results indicated that with the increase of lignin content, not only cross‐linked structures were introduced in CNFs, but also the flexibility were obviously improved, accomplishing for supercapacitor self‐supported electrodes. Moreover, CNFs with different content of lignin varied in fiber morphology and microstructure, harvesting developed porous structure in CNFs. PPL3‐CNF prepared with adding lignin at a mass ratio of 3/7 into a concentration of 10 wt.% pitch/PAN solution presented the highest specific surface area of 949 m2 g−1, exhibiting an excellent specific capacitance of 190.7 F g−1 at 0.2 A g−1.

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Analysis of the dispersion state of pitch particles in polymers for nanofiber fabrication by optical microscopy and image processing

Cited by 7 publications

References 29 publications

Electrospun Polymer Nanofibers: Processing, Properties, and Applications

Electrospun Polymer Nanofibers: Processing, Properties, and Applications

Deep Learning and Computer Vision: Machine Learning Analysis and Image Processing of Puromycin Treated Microscopy

Effect of adding lignin on preparation and properties of pitch/PAN‐derived electrospun nanofibers inducing cross‐linking structure

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