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

Scientific Reports 2023 15 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 45 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Marta Elisabetta Eleonora Temporiti, Marta Elisabetta Eleonora Temporiti, Flavio Cognigni, Lidia Nicola, Nicolas Guéninchault, Marta Elisabetta Eleonora Temporiti, Lidia Nicola, Nicolas Guéninchault, Nicolas Guéninchault, Nicolas Guéninchault, Lidia Nicola, Solveig Tosi, Marco Rossi Nicolas Guéninchault, Nicolas Guéninchault, Solveig Tosi, Solveig Tosi, Marco Rossi Marco Rossi

Summary

Researchers used advanced 3D X-ray microscopy combined with deep learning to visualize how the fungus Fusarium oxysporum physically penetrates and begins breaking down PET plastic, finding it preferentially attacks edges and corners where it can enter through cracks. This detailed imaging approach reveals fungal-plastic interactions that were previously impossible to observe, offering insights for developing biological plastic degradation methods.

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.

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