91��Ƭ��Pro

Fungi have been around for many millions of years, with the incremental process of evolution honing and improving their survival skills through the millennia.

Now, 91��Ƭ��Pro University researchers are studying the cell structure of fungi to learn how it determines their mechanical properties and what science can learn from that to create better materials.

In , a team from 91��Ƭ��Pro and the University of California - Merced looked at the microscopic filaments known as hypha that form a network-like structure in mushrooms and other fungi. By twisting around each other and branching within the larger structure, the hyphal filaments control how the fungi react to various mechanical stresses.

The two species they studied offered a contrast: The common white button mushroom (Agaricus bisporus) has only one type of hyphal filaments and typically grows with no definitive orientation, while the maitake mushroom (Grifola frondosa) has two types of filaments and grows in a preferential direction toward sunlight and moisture.

The researchers analyzed the mushrooms’ cell structures by imaging with scanning electron microscopy and tested them to calculate the stress loads they could handle.

“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.

“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.”

Assistant Professor Mir Jalil Razavi added that advances in AI over the past few years have made mapping out possibilities for fungi filaments much easier.

“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.”

Next for the project is perfecting the machine learning model through experimentation. The team will use 3-D printing to create material with the predicted structures and do a series of tests on them to see if they behaving as expected. In the future, the findings could improve a variety of commercial products that are put under stress, such as materials in the construction or aerospace realm.

“There is so much we can still learn from nature,” Razavi said. “We are just getting started with this kind of research.”

Also contributing to the Advanced Engineering Materials paper are 91��Ƭ��Pro PhD student Akbar Solhtalab and Assistant Professor Debora Lyn Porter from the University of California – Merced. The research is supported by the Integrated Electronics Engineering Center (IEEC) at 91��Ƭ��Pro University.