Papers by Author: Qing Li

Abstract: Cellulose micro/nano fibrils generated from biomass are relative new reinforcing materials for polymer composites, which have potential lightweight and high strength and are renewable. In the present study, the preparation method of extracting cellulose micro/nano fibrils from wood was introduced. After successful disintegration, the morphological characteristics of the wood fibers, purified cellulose fibers, cellulose fibers activated by ultrasonic-wave and cellulose micro/nano fibrils after homogenization treatment, were compared by visual examination and scanning electron microscopy. The results showed that cellulose micro/nano fibrils have been efficiently extracted from wood, which have great potential in the application areas of papermaking, bio-nanocomposites, food, cosmetics/skin cream, medical/pharmaceutical, and so on.

Abstract: Microfibrillated cellulose (short for MFC) has rapidly advanced due to its unique nano-order-unit web-like network structure and such special properties as extremely high strength, large specific surface area and high aspect ratio. In the present paper, the processing technology and the research progress of the preparation methods of MFC with mechanical and it combined with enzymatic treatment are briefly reviewed, then the four main challenges about preparation of MFC are figured out. In the last section, the direction of this research field is also highlighted.

Abstract: In this paper, the production technology of laminated veneer lumber was optimized by using the uniform design method. The effects of production technology, such as the press duration, the press temperature, the press pressure and the adhesive spread, were found statistically significant on some mechanical properties of LVL. So in this paper, the thickness of LVL board was a fixed value, but the press duration, the press temperature and the adhesive spread were variable. The modulus of elasticity (MOE) and static bending strength (MOR) as predicted by the ANN approach and the regression functions were compared with the experimental values. It was shown that the proposed neural network model was able to predict valuable mechanical properties, such as the modulus of elasticity and static bending strength, that would facilitate the development of optimum design of properties for manufacturing high quality materials.