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Bonding Mechanism of Bamboo Particleboards Made by Laccase Treatment 被引量:2
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作者 Jin Wang Weigang Zhang xiaowei zhuang 《Journal of Renewable Materials》 SCIE EI 2021年第3期557-568,共12页
Using 1–8 years bamboo as materials,the content of different chemical constituent was tested,and the reactive oxygen species(ROS)free radicals produced from laccase treated bamboo were detected by electron spin-reson... Using 1–8 years bamboo as materials,the content of different chemical constituent was tested,and the reactive oxygen species(ROS)free radicals produced from laccase treated bamboo were detected by electron spin-resonance(ESR)spectroscope.The wet-process particleboard was made from laccase-treated bamboo by hot pressing and board mechanical properties including internal bond strength(IB),modulus of rupture(MOR)and thickness swelling(TS)after 2-hours water absorption were tested under different conditions.Results showed that laccase mainly catalyze the bamboo components and improved the bonding strength of laccase-treated boards.By ESR measurement on each single component such as milled bamboo lignin,xylan and pore cotton treated with laccase,it was proved that laccase helped the degradation of bamboo lignin to produce ROS free radicals and could not catalyze the oxidation of cellulose and hemicelluloses.A logarithmic function relationship was found between board mechanical properties and ROS free radical level.It is optimal to using 5-year-old bamboo for high efficient utilization.The laccase treatment improves the activity of bamboo particles participating in self-adhesion reaction. 展开更多
关键词 Bamboo particleboard LACCASE mechanical properties ROS ESR
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光镊探索DNA凝聚过程
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作者 xiaowei zhuang 韩旭(译) 《生物技术通报》 CAS CSCD 2008年第2期214-215,共2页
自从20年前光镊技术被Ashkin、Chu及其同事发明,该技术已被用于各种生物学研究并由此获得丰富信息。例如应力下生物高分子的行动,DNA连接酶如何译码或如何消化DNA,动力蛋白如何沿分子轨道移动,以及RNA和蛋白质分子如何折叠/展开。
关键词 DNA连接酶 光镊技术 凝聚过程 生物高分子 蛋白质分子 分子轨道 动力蛋白 生物学
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Elucidating Structures of Complex Organic Compounds Using a Machine Learning Model Based on the 13C NMR Chemical Shifts
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作者 Anan Wu Qing Ye +4 位作者 xiaowei zhuang Qiwen Chen Jinkun Zhang Jianming Wu Xin Xu 《Precision Chemistry》 2023年第1期57-68,共12页
We present a protocol that combines the support vector machine(SVM)model with accurate 13C chemical shift calculations at the xOPBE/6-311+G(2d,p)level of theory,denoted as SVM-M(i.e.,SVM for magnetic property).We show... We present a protocol that combines the support vector machine(SVM)model with accurate 13C chemical shift calculations at the xOPBE/6-311+G(2d,p)level of theory,denoted as SVM-M(i.e.,SVM for magnetic property).We show here that this SVM-M protocol is a versatile tool for identifying the structural and stereochemical assignment of complex organic compounds with high confidence.Of particular significance is that,by utilizing the dual role of the decision values in SVM,the present SVM-M protocol provides an accurate yet efficient solution to simultaneously handle the classification issue(i.e.,“is a given structure correct or incorrect?”)and the comparison-based problem(i.e.,“which structure is more likely to be correct or wrong among several candidate structures?”).A significantly high success rate has been reached(i.e.,∼100%on a set of 760 sample molecules with 1592813C chemical shifts),which makes the SVMM protocol a powerful tool for routine applications in structural and stereochemical assignments,as well as in detecting misassignments,for complex organic compounds,including natural products. 展开更多
关键词 NMR Machine learning Structure elucidation Support vector machine DFT
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