Mycelium-based wood composites for light weight and high strength by experiment and machine learning
Mycelium-based wood composites for light weight and high strength by experiment and machine learning
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DOI：
10.1016/j.xcrp.2023.101424
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发表时间：
2023-05
影响因子：
8.9
通讯作者：
Libin Yang;Zhao Qin
中科院分区：
文献类型：
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作者：
Libin Yang;Zhao Qin
由复合木纤维组成的木材复合材料在机械强度上严重依赖于合成胶粘剂。在这里，我们着重于利用菌丝体获得木材复合材料，并将实验和机器学习相结合，以获得更好的力学性能。我们通过形成次级纤维网络来培养菌丝体，使其成为一种天然的粘接剂。我们发现菌丝体增强了复合力学，但在高温下会分解。我们获得的复合材料样品的极限强度高达12.99 MPa，杨氏模量为3.66 GPa，高于在相同条件下获得的无菌丝体样品。我们建立了基于实验测试的机器学习模型，以预测任何处理条件下的材料功能。含有菌丝体的复合材料需要相对较低的温度、较高的压力和较短的压制时间才能获得较高的强度和模量。我们的研究结果可能对生物材料的工程复合材料有用。
Wood composites composed of recombined wood fibers heavily depend on synthetic adhesives for mechanical strength. Here, we focus on using mycelium to gain wood composites and integrating experiments and machine learning for better mechanical properties. We grow myceliumPleurotus eryngiion stalk fibers as a natural adhesive by forming a secondary fibrous network. We find that mycelium enhances the composite mechanics but breaks down at high temperatures. We obtain composite samples with an ultimate strength of up to 12.99 MPa with a Young's modulus of 3.66 GPa, which is higher than samples without mycelium obtained from the same condition. We build machine learning models based on experimental tests to predict the material functions for any treatment conditions. The composite with mycelium requires a relatively lower temperature, higher pressure, and shorter pressing time to yield higher strength and modulus. Our results could be useful for engineering composites from living materials.