Monash University researchers have developed an artificial intelligence (AI) program to assist in the global fight against environmental microplastics.
The program has been described as an important first step in finding ways to clean up microplastics and to prevent them from entering environmental waterways.
The program uses machine learning algorithms to analyse thousands of samples in fractions of a second, a process that can take months for humans, to gain a crucial understanding of where and how to act.
The algorithm uses the chemical components that make up materials to identify characteristic ‘signatures’ (complex numerical figures, many thousands of characters long) that can accurately identify known microplastic types.
The program was developed by lead researcher, Monash University PhD candidate, Frithjof Herb, and supervisor, senior lecturer in the Monash School of Chemistry Dr Khay Fong.
Herb said the researchers are addressing a significant bottleneck for progress in tackling the microplastics problem.
“Not only is the process of analysing samples arduous and time consuming, but until now, we have been unable to do it on a large enough scale to gain a comprehensive understanding of exactly what microplastics we are dealing with, where they are, and where they end up,” said Herb.
Natural fibres commonly mistaken for microplastics include algae, animal fur or crustacean shells and seashells.
Herb said the evolution of human-made materials also complicates things, with chemical components of microplastics constantly changing.
“Plastics are constantly changing, both in how they’re made and how they break down in the environment. Traditional tools struggle to keep up with these changes,” said Herb.
“But our tool offers a crucial advantage to scientists who need something that can quickly adapt, which is important for analysing data that continues to evolve.
“We are really proud of what we have achieved here; it runs nicely on conventional laptops, reflecting our focus on sustainability and accessibility, which we sought through small and efficient models.”
For more information, https://www.monash.edu/
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