Mycelium characterization and Image Analysis

Mycelium is a new biomaterial whose microstructure consists of fibers, the hyphae, that grow and eventually connect, creating a mesh that resembles an open cell foam structure. The purpose of this project is to extract structural features and develop a QSPR for mycelium transport properties with the aim to design a mycelium-based high performance membrane. Seven key features of transport scaling laws in porous media were extracted from SEM images of a mycelium sample. This research started as a Master project, conducted under supervision of Dr. P. Nalam and Dr. O. Wodo, in collaboration with Ecovative Design.

T. Stona de Almeida. Preprint:

Abstract: Mycelium is a promising biomaterial based on fungal mycelium, a highly porous, nanofibrous structure. Scanning electron micrographs are used to characterize its network, but the currently available tools for nanofibrous microstructures do not contemplate the particularities of biomaterials. The adoption of a software for artificial nanofibrous microstructure for mycelium characterization adds the uncertainty of imaging artifact formation to the analysis. The reported work combines supervised and unsupervised machine learning methods to automate the identification of artifacts in the mapped pores of mycelium microstructure.

Conclusion: The proposed protocol for automated detection of imaging artifacts is consistent with the analytical approach of data cleaning based on expert knowledge and its extension to a larger dataset and double-supervised classification of artifacts by experts is encouraged. Transfer learning could be a potential machine learning method to generalize and automate the classification to future micrograph additions to the database.

Keywords: Machine learning; unsupervised learning; image processing; mycelium; microstructure informatics.

Paper in submission with J Bie-Kaplan, FZ Chaggan, Z Corey, E Gawronska, P Manimuda, G McIntyre, E Olivero, PC Nalam*, O Wodo*

Abstract: Mycelium has the potential to form a natural gradient porous structure. This paper, for the first time, presents the detailed structure-property characterization at the micro-length scales. We show the structural variation of mycelium along the growth direction. We quantify the structure with a series of structural descriptors and report the gradual changes in the morphologies along with the growth directions. Our results demonstrate the potential to use mycelium as a gradient structure for multiple applications, including membranes, bio-scaffolds, and structural material.

Keywords: Gradient porous networks; mycelium; micromechanics; image processing; microstructure informatics

*Corresponding authors

Master thesis supervised by Dr. P. Nalam and Dr. O. Wodo

Mycelium is a renewable biomaterial, a composite often considered as an alternative to synthetic polymers. It can be prepared by growing the structure in molds: agricultural waste, nutrients and liquid mushroom mycelium are mixed and put in a mold. Once its growth has achieved the desired size, the material is demold and baked. The baking process makes the material inert and dry, killing the mushroom and keeping the designed mold shape. When disposed, it can be used as plant nutrient. Mycelium has been employed in faux leather, structural boards and packaging. Mushroom mycelium is the root structure of mushrooms. It consists of a porous structure of fibers, the hyphae, which have typical diameter in the range of a few to several microns, and length in the range of a few microns to several meters, depending on the species and growth conditions. Mechanics of mycelium have been studied, however transport properties have not been explored to our best knowledge. The objective is to characterize SEM images of several mycelium samples of the species Ganoderma resinaceum. To achieve this aim we leveraged tools of materials informatics e.g. skeletonization, feature extraction, clustering. The goal is to design mycelium high performing membranes.

Poster presented at MRS Fall 2019 (MT03.10.03) with E. Oliverio, O. Wodo, P. Nalam, J. Bie-Kaplan and G. McIntyre.

The discovery and design of novel structures for reactive membranes which purify or enrich contaminated air, either without or with limited use of toxic chemicals, still have a significant environmental impact. Airborne byproducts of manufacturing and fuel combustion such as particulate matter (PM2.5; particulate diameter < 2.5 micron) have proven to be a global health risk and while current filtration membrane materials such as polyester and fiberglass benefit from tunable pore areas and fine fiber diameters, they are non-recyclable and must be replaced regularly due to fouling caused by the accumulation of pollutants. In collaboration with Ecovative Design, a bio-fabrication company working with mycelium (the root structure of mushroom), we studied the application of mycelium films as air-based filtration membranes. Surface proteins on mycelium hyphae are bio-adsorbants of several heavy metals and air contaminants and are therefore ideal candidates for membrane development. Like other naturally growing materials, mycelium has a heterogeneous structure, and in its optimization for membrane design with high filtration efficiency, a comprehensive quantification of its structure is necessary. In this study, through a combination of high-resolution and high-throughput imaging of the membrane, across several location and depths of the membrane, we quantitatively estimated several physical parameters such as pore area, fiber diameter, network topology, and fiber orientation of these heterogeneous membranes. Scanning Electron Microscope and atomic force microscope images were acquired to provide micron-level details of the mycelium network. These images were sampled across a range of magnifications, and image processing techniques such as statistical region mapping and axial thinning were employed for feature extraction. By obtaining a distribution of the fibers radii, Gaussian mixed models were used to identify three unique fibers indicating bifurcation as the main network growth mechanism. Additionally, unsupervised learning tools were employed to appropriately identify pores from the processed images, which showed a positively skewed data with an average pore area of 4micron^2 and mode 0.5micron^2 across the growth. These pore areas put mycelium in the magnitude for PM2.5 filtration, verifying mycelium’s potential as an air filtration membrane. The results accelerate the development of mycelium-based biofiltration products by establishing a feedback loop with Ecovative Design to optimize their growth conditions and species selection to generate optimal filtration microstructures.