Gu Kongjie, Zhang Xingying, Dong Zhiqiang, Chen Hongyun, Xu Manqi, Sun Zhuolin, Han Shenjie, Zhang Jieyu, Yu Youming, Hou Junfeng
Zhejiang A&F University, College of Chemistry and Materials Engineering, Hangzhou 311300, PR China; Key Laboratory of Wood Science and Technology of Zhejiang Province, Hangzhou 311300, PR China.
Zhejiang Jianyang Polymer Technology Co., LTD, Jiaxing 314512, PR China.
Int J Biol Macromol. 2025 May;306(Pt 3):141690. doi: 10.1016/j.ijbiomac.2025.141690. Epub 2025 Mar 3.
Wood and its derivatives play a decisive role in traditional Chinese architecture. Waste wood as a major source of garbage in the construction industry represents a valuable source. The efficient recycling of waste wood has become an urgent technical problem in waste recycling research. Herein, we report a facile method to develop a high-performance biomass-based flame-retardant composite from waste wood bonded with isocyanate adhesive. The phytic acid and tannic acid were used as bio-based flame retardants. The effects of flame-retardant type and quantity on the flame retardancy, smoke suppression, and mechanical properties of the composites were investigated. Furthermore, the flame-retardant properties of the composite were also predicted using a deep-learning model. The optimal flame-retardant addition of 9 wt% endows the composites with enhanced flame retardancy, smoke suppression, and superior mechanical properties. A heat release rate prediction model was developed using a long short-term memory network with R ranging from 0.94 to 0.99, indicating that the model can effectively predict the combustion performance of materials. This study supports the high-value utilization of waste wood through deep learning, contributing to the green and low-carbon development of the construction industry.
木材及其衍生物在中国传统建筑中起着决定性作用。废木材作为建筑行业垃圾的主要来源，却是一种宝贵的资源。废木材的高效回收利用已成为废弃物回收研究中亟待解决的技术问题。在此，我们报告了一种简便的方法，即利用异氰酸酯粘合剂粘结废木材来制备高性能生物质基阻燃复合材料。植酸和单宁酸被用作生物基阻燃剂。研究了阻燃剂类型和用量对复合材料阻燃性、抑烟性和力学性能的影响。此外，还使用深度学习模型预测了复合材料的阻燃性能。9 wt%的最佳阻燃剂添加量赋予了复合材料增强的阻燃性、抑烟性和优异的力学性能。使用长短期记忆网络开发了热释放速率预测模型，R值在0.94至0.99之间，表明该模型能够有效预测材料的燃烧性能。本研究通过深度学习支持废木材的高价值利用，为建筑业的绿色低碳发展做出贡献。
