Review on natural plant fibres and their hybrid composites for structural applications: Recent trends and future perspectives

Effects of heat treatment on the chemical compositions and thermal decomposition kinetics of Japanese cedar and beech wood.

In this study, four kinds of lignocellulosic fibers (LFs), namely, those from Chinese fir (Cunninghamia lanceolata), Taiwan red pine (Pinus taiwanensis), India-charcoal trema (Trema orientalis) and makino bamboo (Phyllostachys makinoi), were selected as reinforcements and incorporated into high-density polyethylene (HDPE) to manufacture wood-plastic composites (WPCs) by a flat platen pressing process. In addition to comparing the differences in the physico-mechanical properties of these composites, their chemical compositions were evaluated and their thermal decomposition kinetics were analyzed to investigate the effects of the lignocellulosic species on the properties of the WPCs. The results showed that the WPC made with Chinese fir displayed a typical M-shaped vertical density profile due to the high aspect ratio of its LFs, while a flat vertical density profile was observed for the WPCs made with other LFs. Thus, the WPC made with Chinese fir exhibited higher flexural properties and lower internal bond strength (IB) than other WPCs. In addition, the Taiwan red pine contained the lowest holocellulose content and the highest extractives and α-cellulose contents, which gave the resulting WPC lower water absorption and flexural properties. On the other hand, consistent with the flexural properties, the results of thermal decomposition kinetic analysis showed that the activation energy of the LFs at 10% of the conversion rate increased in the order of Taiwan red pine (146–161 kJ/mol), makino bamboo (158–175 kJ/mol), India-charcoal trema (185–194 kJ/mol) and Chinese fir (194–202 kJ/mol). These results indicate that the morphology, chemical composition and thermal stability of the LFs can have a substantial impact on the physico-mechanical properties of the resulting WPCs.

https://www.mdpi.com/2073-4360/9/12/726/pdf

Characterization of Wood-Plastic Composites Made with Different Lignocellulosic Materials that Vary in Their Morphology, Chemical Composition and Thermal Stability

Effects of density and heat treatment on the physico-mechanical properties of unidirectional round bamboo stick boards (UBSBs) made of Makino bamboo (Phyllostachys makinoi)

Biodegradable materials are increasingly essential in modern society due to their environmentally friendly characteristics. Although poly(butylene adipate-co-terephthalate) (PBAT) has been evaluated as a suitable material for packaging and mulching, its high production cost and decrease in physical properties due to UV irradiation hinder its industrial application. In this study, lignin was used as an eco-friendly additive to address these issues, and acetylation was attempted to prevent the decrease in film properties caused by the self-aggregation of lignin. Through a simple acetylation process, lignin was 80.3% modified, as confirmed by FTIR and 31P NMR analysis. The resulting acetylated lignin showed excellent dispersibility and compatibility in the PBAT matrix, maintaining the composite film's mechanical and thermal properties even at 10% lignin content. The addition of acetylated lignin provided UV-blocking performance and thus strongly prevented the photodegradation of PBAT. Furthermore, the addition of acetylated lignin hindered the movement of infiltrated molecules within the composite, enhancing the oxygen and moisture barrier properties. As a result, the food packaging performance of PBAT/acetylated lignin films was enhanced. The use of PBAT/acetylated lignin composites is expected to provide a effective way of using lignin as a biodegradable polymer for industrial applications and a solution to overcome the disadvantages of existing PBAT-based biodegradable plastics. 

Enhanced barrier properties of biodegradable PBAT/acetylated lignin films

This study compared and characterized slicewood acetylation with acetic anhydride using conventional liquid phase, microwave, and vapor phase reactions. The results revealed that there were no significant differences in the flexural properties between the unmodified and acetylated slicewood, regardless of the reaction method used. Furthermore, findings concluded that the slicewood acetylated with a vapor phase reaction required less modifying agent to achieve high levels of weight percent gain (WPG). The correlations between the anti-swelling efficiency (ASE) and the WPG of the slicewood acetylated with a vapor phase reaction were higher than the conventional liquid phase and microwave reactions. The reactivity of the cellulose hydroxyl groups was sensitive to the C2 and C6 reactive sites for all three acetylation methods. However, the reactivity was more pronounced at the C2 position with a vapor phase reaction compared to the other two reactions. Nevertheless, slicewood acetylation with acetic anhydride was found to not have a significant influence on viscoelastic properties for any of the different reaction methods.

https://ojs.cnr.ncsu.edu/index.php/BioRes/article/download/BioRes_09_4_6463_Yang_Slicewood_Acetylation_Comparison/3050

Comparisons and Characteristics of Slicewood Acetylation with Acetic Anhydride by Liquid Phase, Microwave, and Vapor Phase Reactions

This study was compared and characterized two different alkali (potassium carbonate (PC) and potassium acetate (PA))-catalyzed acetylations of slicewood with vinyl acetate (VA) by a vapor phase reaction. The results revealed that the esterification reaction between VA and the hydroxyl groups of slicewood could be improved by using PC or PA as a catalyst. Additionally, a significant weight percent gain was obtained after VA acetylation with 5% of catalyst. Furthermore, the reactivity of the cellulose hydroxyl groups for VA acetylation was more pronounced at the C2 reactive site compared to acetylation with acetic anhydride. On the other hand, the apparent activation energy of thermal decomposition between 10% and 70% conversion is 174-183, 194-200, and 183-186 kJ/mol for unmodified slicewood and VA-acetylated slicewood with PC and PA, respectively. Accordingly, the thermal stability of the slicewood could be effectively enhanced by VA acetylation, especially for using the PC as a catalyst.

Characterization and Thermal Stability of Acetylated Slicewood Production by Alkali-Catalyzed Esterification.

Robust secret image sharing scheme resistance to maliciously tampered shadows by AMBTC and quantization.

With the rapid development of medical industry, medical imaging related equipment is becoming more and more advanced, image data are becoming larger and larger, and it is more and more common for medical images to be stored and viewed in the cloud. Therefore, effective image compression of medical images plays a vital role in today's medical information system. In this paper, a variational self-encoder based on deep learning is proposed to compress medical images. We have carried out image compression experiments on CHESTX-Ray8 data set of the National Institute of Health (NIH), and compared the performance with another deep learning compression method and the compression method without deep learning. Experimental results show that this algorithm outperforms other methods in Peak Signal-to-Noise Ratio (PSNR).

X-Ray Image Compression Using Variational Auto-encoder

As machine vision involves many fields such as photogrammetry, national defense and military, and security surveillance, fisheye cameras have also been widely used as an ultra-wide-angle lens. Different from ordinary cameras, fisheye cameras can obtain a wider viewing angle, but their imaging laws will produce nonlinear distortion, which needs to be corrected to achieve better visual effects. In this paper, a fisheye distortion correction method based on deep learning is proposed. According to the parameter division model, the distortion dataset is synthesized, the neural network is constructed, and the parameter model is trained. Finally, the fisheye image is corrected by extracting the distortion features and estimating the distortion parameters. Image enhancement. This method overcomes the limitation of traditional correction methods that rely heavily on imaging model or camera calibration, benefits from the learning features of neural networks, and has better universality; The results show that this method has better performance.

A fisheye distortion correction method based on deep learning

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