A research team from Binghamton University and the University of California – Merced is currently investigating how the cellular structure of fungi influences their mechanical properties. The goal is to develop new approaches for designing load-bearing materials that could be used in 3D printing. The focus lies on hyphal filaments, which form the so-called mycelium. These thread-like structures branch and intertwine within the fungal tissue and play a key role in how the organism responds to mechanical stress.
In their study, published in the journal Advanced Engineering Materials, the researchers analyzed the cellular architecture of two fungal species: the common white button mushroom (Agaricus bisporus) and the more structurally complex maitake mushroom (Grifola frondosa). While Agaricus exhibits a uniform hyphal network without a preferred orientation, Grifola features two distinct filament types that grow preferentially toward environmental stimuli such as light and moisture. Using scanning electron microscopy, the team produced high-resolution images and conducted mechanical stress tests in subsequent steps.
“Moving forward, the first step involves developing a finite element model — a computational framework that enables mechanical property testing and analysis in the second phase,” said Mohamed Khalil Elhachimi, MS ’24, a PhD student at the Thomas J. Watson College of Engineering and Applied Science’s Department of Mechanical Engineering who served as first author on the research.
Assistant Professor Mir Jalil Razavi said: “The third phase is direct design, so we have a model that predicts the mechanical behavior based on the structure. And the last one is inverse design, where we define the mechanical properties and the machine learning model predicts the structure that exhibits this mechanical property.”
The next stage involves developing a finite element model based on a 3D Voronoi tessellation to simulate mechanical behavior in detail.
“This kind of inverse design is possible only with deep learning models — for example, computing 10,000 filaments, their locations and their orientations,” Razavi said. “This is something that AI can do once we run simulations to train the model.”
Advances in deep learning now make it possible to simulate the behavior of tens of thousands of filaments with precise structural attributes.
“There is so much we can still learn from nature,” Razavi said. “We are just getting started with this kind of research.”
The project may have long-term applications in sectors such as construction and aerospace.
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