High-temperature flexible, strength and hydrophobic YSZ/SiO2 nanofibrous membranes with excellent thermal insulation

Peng, Ying; Xie, Yongshuai; Wang, Lin; Liu, Lixin; Zhu, Silun; Mao, Dehua; Zhu, Luyi; Zhang, Guanghui; Wang, Xinqiang

doi:10.1016/j.jeurceramsoc.2020.09.071

Cited by 66 publications

(39 citation statements)

References 70 publications

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“…Figure 7C illustrates that the hot surface temperature of the ZAT samples near the butane ame could reach more than 1300 ºC. The surface away from the ame served as the cold surface [59]. In Fig.…”

Section: Mechanical and Thermal Properties Of Zat Berboardsmentioning

confidence: 95%

Preparation of Al2TiO5 ceramic fibers and thermal expansion, insulation, and strength of ZrO2-Al2TiO5 fiberboards

Wei

Xie

Deng

et al. 2021

Preprint

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In the field of thermal insulation, Zirconia (ZrO2) fiber and its products have high thermal expansion coefficients and poor thermal shock resistance, which leads to great challenges in the industry. In this work, aluminum titanate (Al2TiO5) single-phase ceramic fibers were prepared by a sol-gel method, using polyacetylacetone titanium-aluminum (PAAT) as the precursors with electrospinning technology. The single-phase Al2TiO5 fibers exhibited excellent high-temperature resistance, and maintained fibers morphology at a high temperature of 1500 ºC. Next, ZrO2-Al2TiO5 (ZAT) fiberboards were prepared by mixing ZrO2 fibers and Al2TiO5 fibers. The mixture was heat-treated to 1500 ºC to prepare the ZAT fiberboards. Characterization results indicated that the contents of Al2TiO5 fibers have obvious effects on the performance of thermal insulation, strength, thermal expansion, and thermal shock resistance. Particularly, the thermal expansion coefficient of ZAT-8 fiberboards was about 20% lower than that of ZAT-0 fiberboards. These attractive characteristics might give ZAT fiberboards enormous potential in the field of high-temperature insulation.

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Section: Mechanical and Thermal Properties Of Zat Berboardsmentioning

confidence: 95%

Preparation of Al2TiO5 ceramic fibers and thermal expansion, insulation, and strength of ZrO2-Al2TiO5 fiberboards

Wei

Xie

Deng

et al. 2021

Preprint

Self Cite

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“…Related scholars use deep neural networks (DNNs) to extract clear edges from blurred images, which greatly simplifies the estimation of the fuzzy kernel and reduces the amount of calculation [16]. Aiming at the problem of estimating the fuzzy kernel in the traditional blind deblurring algorithm, the researchers proposed a scale recursive network SRN-DeblurNet to remove image blur, and used a complex fuzzy data set for verification and achieved good results [17]. Since the removal of image blur based on the deep learning method requires a huge dataset to train the model, there is not so much fuzzy data that can be trained, which limits the application of the deep learning method to a certain extent.…”

Section: Related Workmentioning

confidence: 99%

Insulator Hydrophobic Image Edge Detection Algorithm considering Deconvolution and Deblurring Algorithm

Wang¹,

Ma²

2022

Mathematical Problems in Engineering

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In this paper, the Gram matrix is used to calculate the correlation of the filter response sets under different scale kernels learned by each layer of the network in the deconvolution, and the loss between the corresponding feature response correlations in the multilayer network is calculated. Linear summation is used to obtain a stable, multiscale image model representation. This paper extracts the contours of the salient areas of the image and adjusts the parameters of the deconvolution network to learn the salient area patterns of the image. At the same time, for the image to be generated, a shape template is used to limit the range of the area to be generated in order to obtain a shape image with similar patterns. When the spatial relative relationship characteristics of the image constituent objects are obvious, we appropriately add high-level semantic feature activation values for reinforcement. This paper solves the estimation of the unknown blur kernel by using image prior knowledge, filtering and gradient domain algorithms and other different technologies to obtain image jitter or scene movement information and estimate the size, location, and density of the blur kernel. This paper studies a relatively robust deconvolution model, which is insensitive to random noise, has stable effects, and can overcome the water ripple effect caused by the usual convolution process. This paper attempts to study the fuzzy model with variable space. The usual blur is a spatial invariant model; that is, a single kernel is used to describe the motion of all pixels on the image. By selecting different characteristic parameters, this paper conducts experimental research on some existing hydrophobic indicator function methods and calculates the relationship between characteristic parameters and hydrophobicity when different hydrophobic indicator functions are adopted. One characteristic of the hydrophobic image of composite insulators is low contrast. The traditional method of converting color images to grayscale images cannot improve the image contrast. This paper analyzes the hydrophobic image of the composite insulator, and the extracted B channel component image of the hydrophobic image improves the contrast of the image and facilitates the subsequent segmentation of water traces and background. In this paper, the water repellent image's watermark area is counted, and connected-domain wave processing is used to limit the area of water droplets retained, thereby improving the efficiency of filtering water droplets without having a big impact on the image as a whole. The problem of uneven illumination is an unavoidable problem in the field of image processing, and the resulting reflection problem brings difficulties to image processing. This article regards the reflective area of the watermark as a “hole” and uses the idea of “hole filling” to eliminate the reflective point, which weakens the reflection problem to a certain extent.

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“…The unique properties of CNFs such as high surface area, extraordinary length, low density, high porosity, and thermo-mechanical properties [40,41] qualify them for many different applications. This chapter covers a review of their applications related to tissue engineering [35,36,[42][43][44][45][46][47], sensors [32-34, 48, 49], water remediation [50][51][52][53][54][55][56], batteries [57][58][59][60][61][62][63], catalyst supports/catalysts [64][65][66][67][68][69][70][71], electromagnetic interference (EMI) shielding [72][73][74][75][76][77][78][79], and thermal insulation materials [26,[79][80][81][82], etc. A list of the recent studies about ceramic nanofibers by electrospinning method is presented in Table…”

Section: Applications Of Ceramic Nanofibersmentioning

confidence: 99%

“…The resulting ZrO 2 − Al 2 O 3 nanofibrous aerogels displayed a recovery of 90%, compression strength of more than 1100 kPa, temperature-invariant superelasticity, and high fatigue resistance, thermal insulation performance with low thermal conductivity (0.0322 W m −1 K −1 ), and temperature resistance up to 1300°C [82]. Peng et al [80] fabricated yttria-stabilized zirconia mixed silica (YSZ/SiO 2 ) nanofibers by the electrospinning method. The use temperature of the nanofibers increased by nearly 300°C and their maximum strength reached 5.9 ± 0.8 MPa.…”

Section: Thermal Insulation Materialsmentioning

confidence: 99%

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Recent Advances in Applications of Ceramic Nanofibers

Kızıldağ¹

2021

Nanofibers - Synthesis, Properties and Applications

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Ceramic materials are well known for their hardness, inertness, superior mechanical and thermal properties, resistance against chemical erosion and corrosion. Ceramic nanofibers were first manufactured through a combination of electrospinning with sol–gel method in 2002. The electrospun ceramic nanofibers display unprecedented properties such as high surface area, length, thermo-mechanical properties, and hierarchically porous structure which make them candidates for a wide range of applications such as tissue engineering, sensors, water remediation, energy storage, electromagnetic shielding, thermal insulation materials, etc. This chapter focuses on the most recent advances in the applications of ceramic nanofibers.

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High-temperature flexible, strength and hydrophobic YSZ/SiO2 nanofibrous membranes with excellent thermal insulation

Cited by 66 publications

References 70 publications

Preparation of Al2TiO5 ceramic fibers and thermal expansion, insulation, and strength of ZrO2-Al2TiO5 fiberboards

Preparation of Al2TiO5 ceramic fibers and thermal expansion, insulation, and strength of ZrO2-Al2TiO5 fiberboards

Insulator Hydrophobic Image Edge Detection Algorithm considering Deconvolution and Deblurring Algorithm

Recent Advances in Applications of Ceramic Nanofibers

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