Investigation of air plasma generated by surface microdischarge for decellularized porcine aortic valve leaflets modification

Lu, Chen; Dai, Jinchi; Dong, Nianguo; Zhu, Yu; Xiong, Zilan

doi:10.1002/ppap.202000100

Cited by 6 publications

(2 citation statements)

References 29 publications

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“…Surface microdischarge (SMD) has received significant attention because of its broad application prospects in food preservation [1,2], agriculture [3,4], material processing [5,6], and biomedicine [7,8]. SMD is a simple process that allows direct discharge in air without the use of a specific background gas; furthermore, it offers the advantages of low cost, high primarily completed using equipment such as a spectrometer, ozone detectors, and nitrogen oxide detectors.…”

Section: Introductionmentioning

confidence: 99%

Operation-mode recognition of surface microdischarge based on visible image and deep learning

Chen¹,

Peng²,

Xiong³

2022

J. Phys. D: Appl. Phys.

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Discharging images contain useful information regarding the operation mode of surface microdischarge (SMD). To solve the shortcomings of low efficiency, high cost, and long operation time of existing SMD operation-mode recognition methods, a convolutional neural network (CNN) based on deep learning is introduced herein. The visible image library of SMD at different applied voltages, dielectric sheets with different dielectric constants, and dielectric sheets with different thicknesses and exposure times are constructed using a digital camera. The typical structure of a CNN is discussed, and the hyperparameters, including the number of network layers, convolution kernel size, number of neurons in the fully connected layer, and activation function type that affect the recognition accuracy of the CNN are investigated. The optimal structure of the CNN for the SMD operation-mode recognition is obtained via training. The recognition accuracy of the CNN is compared with those of three traditional machine learning methods: support vector machine (SVM), decision tree (DT), and random forest (RF). Test results show that the recognition accuracy based on the CNN is 99.745%, which is better than those of the SVM, DT, and RF. Finally, an SMD operation-mode online recognition method based on the CNN is proposed.

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Section: Introductionmentioning

confidence: 99%

Operation-mode recognition of surface microdischarge based on visible image and deep learning

Chen¹,

Peng²,

Xiong³

2022

J. Phys. D: Appl. Phys.

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show abstract

“…Due to its complex physical and chemical composition, atmospheric-pressure plasma has great potential in various fields, including biomedicine, material modification, and green agriculture. [1][2][3][4][5][6] Research on the nature of plasma and its interaction with substrates could provide a fundamental understanding of the theoretical basics, which could be utilized in practical applications. Accurate diagnostics of the reactive species and their physicochemical processes is particularly important in plasma fundamental research.…”

Section: Introductionmentioning

confidence: 99%

Detection of long‐lived species in plasma‐activated water, based on digital colorimetry

Zou

Han

et al. 2020

Plasma Processes & Polymers

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To fit a specific application, a cheap and effective manner of measurement of chemistries in plasma‐activated water (PAW) is in demand. We propose a digital colorimetry‐based method of quantifying the long‐lived species in PAW using only smartphones and specialized software. Pin−plate discharge and surface microdischarge were used to generate the PAWs. H2O2 and NO2− were selected as representatives for the test. Various color parameters were extracted from images of a solution consisting of testing species and colorimetric reagent. Specialized curve‐fitting strategies were developed during data processing. The results were consistent with those measured by UV–Vis spectrometry, but they provided better performance and simplified operation. Additionally, the proposed PAW generator design can be integrated with the detecting module based on this method.

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On the mechanisms of surface microdischarge plasma treatment of onychomycosis: Penetration, uptake, and chemical reactions

Xiong

Huang

Zhu

et al. 2021

Plasma Processes & Polymers

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Despite becoming increasingly established as a treatment technique, the mechanism underlying the plasma treatment of onychomycosis has yet to be elucidated. Here, we focus on the interactions between the nail plate and a surface microdischarge plasma, including penetration, uptake, and chemical reactions. Results show that long‐lived gaseous species from plasma can penetrate the nail plate effectively and are primarily responsible for the instantaneous antionychomycosis effect. The amount of uptake far exceeded that of penetration. Attenuated total reflection‐Fourier‐transform infrared spectroscopy and X‐ray photoelectron spectroscopy results show that antimicrobial compounds form on the nail surface. Combined with the uptake of active species in the nail plate, these antimicrobial compounds may inhibit microorganism growth, thereby promoting long‐term protection against onychomycosis.

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Investigation of air plasma generated by surface microdischarge for decellularized porcine aortic valve leaflets modification

Cited by 6 publications

References 29 publications

Operation-mode recognition of surface microdischarge based on visible image and deep learning

Operation-mode recognition of surface microdischarge based on visible image and deep learning

Detection of long‐lived species in plasma‐activated water, based on digital colorimetry

On the mechanisms of surface microdischarge plasma treatment of onychomycosis: Penetration, uptake, and chemical reactions

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