Mycelium-based wood composites for light weight and high strength by experiment and machine learning

Libin Yang, Zhao Qin

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

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 mycelium Pleurotus eryngii on 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.

Original languageEnglish (US)
Article number101424
JournalCell Reports Physical Science
Volume4
Issue number6
DOIs
StatePublished - Jun 21 2023

Keywords

  • Young's modulus
  • lightweight
  • machine learning
  • mechanics
  • mycelium
  • toughness modulus
  • ultimate strength
  • wood composite

ASJC Scopus subject areas

  • General Chemistry
  • General Materials Science
  • General Engineering
  • General Energy
  • General Physics and Astronomy

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