Poly(lactic acid) (PLA) is one of the most important biodegradable polymers, however, brittleness severely limits its applications. Thus, strengthening and toughening PLA is a cutting‐edge research for both academia and industry. In this study, rolling process was applied to simultaneously improve the toughness and strength of PLA and its nanocomposites by regulating microstructure of PLA molecular chains. Results showed that crystallinity and orientation of rolled PLA and its nanocomposites were significantly improved. When rolling rate was 50% at 75°C, elongation at break increased from unrolled PLA of 5.9% to 190.1%. When the rolling rate was 50% at room temperature, tensile strength and Young's modulus increased from unrolled PLA of 59.4 and 1763.7 MPa to 70.4 and 2557.6 MPa, respectively. In addition, multiwalled carbon nanotubes (MWCNTs) in PLA/3 wt.%CNT nanocomposite rolled at 75°C for 50% were highly oriented, leading to a significant reduction in resistivity along the rolling direction, which endows it many great potential applications, such as sensor, electromagnetic wave absorption, and electronic packaging.
Conductive polymer composites (CPCs) have been demonstrated to have many advantages. However, agglomeration of conductive fillers or segregation of conductive networks in CPCs always results in weak mechanical properties and limited conductivity. To address this issue, four types of poly(butylene succinate)/multiwalled carbon nanotube (PBS/CNT) CPC with different conductive network structures are prepared: PBS/CNT‐I: melt mixing with hot pressing (145 °C, 10 MPa), PBS/CNT‐II: solution mixing with hot pressing (145 °C, 10 MPa), PBS/CNT‐III: solution mixing with hot pressing (100 °C, 10 MPa), PBS/CNT‐IV: solution mixing with hot pressing (100 °C, 60 MPa). The results show that PBS/CNT‐IV CPC has the highest mechanical properties, conductivity, and thermal conductivity. Tensile strength, elongation at break and Young's modulus of PBS/CNT‐IV are 119%, 58%, and 37% higher than that of PBS/CNT‐III CPC, respectively. Subsequently, PBS/CNT‐IV CPCs with different CNT contents are prepared. The mechanical, crystallization, electrical and thermal conductivities, and rheological properties as well as foamability are studied. Compared with PBS/5.0 wt.% CNT‐IV CPC, the foamed specimen shows 97% enhancement in conductivity. In general, this work offers a simple, environmentally‐friendly approach for manufacturing CPC with high conductivity and good mechanical property and is instructive for construction of a segregated network structure in low‐melt‐viscosity semi‐crystalline polymer.
Pleioblastus chino var. hisauchii is an important ornamental bamboo species that rarely flowers. Studies on the change in its material properties before and after flowering were lacking. In this paper, the anatomical, chemical, and mechanical properties of bamboo culms before and after flowering were studied by using the method of bio-wood science. The results showed that after flowering, the morphology and proportion of the fiber, vessel and vascular bundle decreased, and the openings of pits in the vessel wall were enlarged significantly; the contents of the main components such as extractives, lignin, holocellulose, cellulose and pentosan rose, while the ash content dropped. There was a decrease in density and modulus of rupture, and a pronounced fall in modulus of elasticity, while the microfibril angle and crystallinity increased. In general, the strength of bamboo flowering culms decreased and the ability to transport nutrients increased, which were closely related to the changes in internal structure and properties. This meant that bamboo flowering may be monitored or predicted by significant changes in some properties (such as pits and modulus of elasticity) and provide a reference for further research on the mechanism of flowering senescence and delayed flowering in bamboo.
Plant has high similarity and dense detail information in morphology, color and texture, especially in bamboo species, which consists of ground tissue and vascular bundles, the cross-sectional images of bamboo belong to fine-grained, for this reason, the classification of bamboo species has always required aid from a domain expert. Recently, deep learning and convolutional neural network (CNN) have become a new solution for image recognition and classification, features can be effectively extracted from bamboo images and high accuracy can be outputted. Here, convolutional neural network models were constructed to achieve the rapid classification of bamboo species, meanwhile, to simulate the complex classification of bamboo, identification complexity was artificially added by mixing all images, but the models were still found feasible. These models are trained to identify 45 bamboo types with Top-1 accuracy of 92.14% and Top-5 accuracy of 98.10%, indicating that the models extracted the specific features from the cross-section images efficiently.
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