We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Data_PET_2
AI summary Read the abstract
This dataset supports research using a scanning technique combined with computer learning to detect and measure tiny plastic particles (microplastics) hidden in soil. Better tools for finding microplastics in soil matter because these particles can end up in the crops we eat and water we drink, so improving detection is a step toward understanding and reducing our exposure to them.
This dataset is a subset of the “2T2D spectroscopy and deep learning for soil MPs quantification” data, comprising 2T2D spectra of soil and microplastics (MPs) mixtures.
More Papers Like This
Data_PET_2
AI summary Read the abstract
This dataset provides spectroscopy readings (a way of measuring materials using light) combined with computer analysis to help detect tiny plastic particles, called microplastics, in soil. Since microplastics in soil can end up in the food we eat and water we drink, better tools for finding them are an important step toward understanding and reducing our exposure to these particles.
Data_PET_3_4
AI summary Read the abstract
This dataset supports research into a new method that combines a scanning technique with artificial intelligence to detect and measure tiny plastic particles (microplastics) hidden in soil. This matters because microplastics in soil can end up in the food we grow and eventually in our bodies, so having faster, more reliable ways to spot them is an important step toward understanding and reducing our exposure. Note that this particular entry is the raw data itself, not a study with health findings—it's the foundation other researchers can use to build and test detection tools.
2T2D spectroscopy and deep learning for soil MPs quantification
AI summary Read the abstract
Scientists have developed a computer tool that uses light-based scanning and artificial intelligence to accurately measure how much microplastic is hiding in soil samples. This matters because microplastics can enter our food supply through crops grown in contaminated soil, and having a faster, more reliable way to detect them helps researchers track this pollution and understand potential risks to human health. Note that this dataset and code paper focuses on the technical method itself, not on health outcomes from microplastic exposure.
Data_PP_1
AI summary Read the abstract
This dataset supports research testing whether a scanning technology combined with computer-based pattern recognition can accurately detect and measure tiny plastic particles hidden in soil. This matters because microplastics in soil can work their way into crops and groundwater, and eventually into our food and water supply, so better detection tools help scientists track contamination before it reaches us. Note that this is a technical dataset rather than a study with health findings—it's laying the groundwork for future contamination research.
2T2D spectroscopy and deep learning for soil MPs quantification
AI summary Read the abstract
Scientists have developed a new tool that combines a special light-scanning technique with artificial intelligence to detect and measure tiny plastic particles (microplastics) hiding in soil. This matters because microplastics in soil can end up in the crops we eat and the water we drink, so having a faster, more accurate way to track them helps researchers understand how much plastic pollution is entering our food supply. This dataset and code release makes the detection method available for other scientists to test and build on, which could speed up future research into microplastics and health risks.
Research digests by email
When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.