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Scientists have developed an algorithm, based on the odor-searching strategy used by organisms ranging from bacteria to multicellular eukaryotes to find food, that could help drones or nanobots to find chemical targets.

Researchers have developed a neural network-based solution for automated recognition of chemical formulas on research paper scans.

Observations of airborne hydroxyl and hydroperoxyl radicals in South Korea indicate current models of ozone production are accurate, but further data is needed to understand radical reactivity.

A mathematical model used to describe cell signaling has been adapted to create patient-specific computer models of prostate cancer, which could help in identifying the most effective treatment for each patient.

Researchers have developed a system based on computer vision techniques that allows automatic analysis of biomedical videos captured by microscopy in order to characterise and describe the behaviour of the cells that appear in the images.

Nuclear power generation can play a crucial role in helping the world reach zero carbon emissions by the middle of the century, especially in countries with low wind resources, according to new work.

Researchers have published the first work examining how voice command features on virtual reality headsets could lead to major privacy leakages, known as “eavesdropping attacks.”

An artificial intelligence system is being trained to identify signs athletes may have taken performance-enhancing drugs, and could even be used to detect if urine samples have been switched.

Changes to the chemical composition of saliva caused by COVID-19 can be identified by Raman spectroscopy that, when used in combination with machine learning, can be used to accurately detect SARS-CoV-2.

Scientists found that Sewer slime can accumulate SARS-CoV-2 RNA, which could decompose or slough off later, potentially impacting the accuracy of wastewater epidemiology studies.