Managing the worldwide steady increase in the production of plastic while mitigating the Earth's global pollution is one of the greatest challenges nowadays. Fungi are often involved in biodegradation processes thanks to their ability to penetrate into substrates and release powerful catabolic exoenzymes. However, studying the interaction between fungi and plastic substrates is challenging due to the deep hyphal penetration, which hinders visualisation and evaluation of fungal activity. In this study, a multiscale and multimodal correlative microscopy workflow was employed to investigate the infiltrative and degradative ability of Fusarium oxysporum fungal strain on polyethylene terephthalate (PET) fragments. The use of non-destructive high-resolution 3D X-ray microscopy (XRM) coupled with a state-of-art Deep Learning (DL) reconstruction algorithm allowed optimal visualisation of the distribution of the fungus on the PET fragment. The fungus preferentially developed on the edges and corners of the fragment, where it was able to penetrate into the material through fractures. Additional analyses with scanning electron microscopy (SEM), Raman and energy dispersive X-ray spectroscopy (EDX) allowed the identification of the different phases detected by XRM. The correlative microscopy approach unlocked a more comprehensive understanding of the fungus-plastic interaction, including elemental information and polymeric composition.

Exploring the infiltrative and degradative ability of Fusarium oxysporum on polyethylene terephthalate (PET) using correlative microscopy and deep learning / Cognigni, Flavio; Temporiti, Marta Eleonora Elisabetta.; Nicola, Lidia; Gueninchault, Nicolas; Tosi, Solveig; Rossi, Marco. - In: SCIENTIFIC REPORTS. - ISSN 2045-2322. - 13:1(2023). [10.1038/s41598-023-50199-w]

Exploring the infiltrative and degradative ability of Fusarium oxysporum on polyethylene terephthalate (PET) using correlative microscopy and deep learning

Cognigni, Flavio
Primo
;
Rossi, Marco
Ultimo
2023

Abstract

Managing the worldwide steady increase in the production of plastic while mitigating the Earth's global pollution is one of the greatest challenges nowadays. Fungi are often involved in biodegradation processes thanks to their ability to penetrate into substrates and release powerful catabolic exoenzymes. However, studying the interaction between fungi and plastic substrates is challenging due to the deep hyphal penetration, which hinders visualisation and evaluation of fungal activity. In this study, a multiscale and multimodal correlative microscopy workflow was employed to investigate the infiltrative and degradative ability of Fusarium oxysporum fungal strain on polyethylene terephthalate (PET) fragments. The use of non-destructive high-resolution 3D X-ray microscopy (XRM) coupled with a state-of-art Deep Learning (DL) reconstruction algorithm allowed optimal visualisation of the distribution of the fungus on the PET fragment. The fungus preferentially developed on the edges and corners of the fragment, where it was able to penetrate into the material through fractures. Additional analyses with scanning electron microscopy (SEM), Raman and energy dispersive X-ray spectroscopy (EDX) allowed the identification of the different phases detected by XRM. The correlative microscopy approach unlocked a more comprehensive understanding of the fungus-plastic interaction, including elemental information and polymeric composition.
2023
x-ray microscopy; correlative microscopy; deep learning; mycology; PET
01 Pubblicazione su rivista::01a Articolo in rivista
Exploring the infiltrative and degradative ability of Fusarium oxysporum on polyethylene terephthalate (PET) using correlative microscopy and deep learning / Cognigni, Flavio; Temporiti, Marta Eleonora Elisabetta.; Nicola, Lidia; Gueninchault, Nicolas; Tosi, Solveig; Rossi, Marco. - In: SCIENTIFIC REPORTS. - ISSN 2045-2322. - 13:1(2023). [10.1038/s41598-023-50199-w]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1707570
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