Science fiction turns into reality
Across the planet, corporations and municipalities account for over 30 per cent of accessible and renewable freshwater usage.
They generate colossal amounts of wastewater containing various concentrations of elements such as copper, zinc, titanium, and mercury, which unfortunately make their way into our drinking water.
Now, instead of worrying about what’s in your next glass of tap water, imagine you could point your phone’s camera at it and know exactly what substances lie within.
It sounds like complete science fiction, right? As crazy as it may seem, a team of McGill researchers recently published a ground-breaking paper documenting a new technique that may be able to detect these substances in waterways in real-time.
Hologram technology and AI combination
It involves combining artificial intelligence (AI) with the company’s digital in-line holographic microscopes, also called nano-DIHM technology.
Hologram recording and reconstruction are the two core components of this combined AI-nano-DIHM technology. The nano-DIHM uses holographic recording to send a beam of light via a pinhole and strike a water sample.
A computer then records the diffraction pattern that the object creates after being amplified. Then, two aquatic AI software programmes called Octopus and Stingray recreate and process the hologram.
Octopus and Stingray were trained on hundreds of previously recorded holograms; by the conclusion of the process, each software could precisely identify the make-up of any sample that was shown to them.
When evaluated on their capacity to recognise oil droplets in combinations of metal oxides, the AIs recorded an accuracy of above 98%.
Programme advantages
The programme helps researchers describe the properties of the water in addition to defining the makeup of a sample.
According to Parisa Ariya, a professor in the departments of chemistry and atmospheric & oceanic sciences at McGill and one of the study’s principal investigators, “for the pollutants […] the things you do not know exist, [the programme] would be able to recognise them.
” It has a lot of potential since by categorising what you already know, you can physically identify what you don’t know.
The determination of the water composition, however, goes beyond identifying only physical traits like size and shape.
“We are working at doing chemical composition as well [as] it allows, for example, [for the further study of fields] from medicine and pharmaceutics to aerosol and climate change, to pollution in air and water,” Ariya explained.
More remarkable is nano-DIHM’s processing speed—its limits are based solely on the computational power provided. computational power provided.
“This technology allows us to do two things—one of them is form a sensor that will be detecting contaminants [using] AI […] in the blink of an eye, 32 milliseconds, and we can do better than that,” Ariya said.
computational power provided. “This technology allows us to do two things—one of them is form a sensor that will be detecting contaminants [using] AI […] in the blink of an eye, 32 milliseconds, and we can do better than that,” Ariya said.
“COVID served as a catalyst,” Ariya said. “We wanted to serve humanity better, and we […] also […] got our alarms up. We knew that a […] major part […] was airborne, and we wanted to provide solutions.”
Competition for Nano-DIHM exists. Compared to the prior method of choice, scanning transmission electron microscopy (S/TEM), nano-DIHM has a lower image resolution. S/TEM microscopes, however, are significantly more expensive and not portable.
They can range in price from $60,000 to $250,000 USD and weigh up to 80 kilogrammes. They can also be up to half a metre tall.
Researchers can bring nano-DIHM on-site and use the technique there because it is physically much smaller than S/TEM and can work with live and moving samples, such as rushing water.
This should shorten the time needed for data collection and analysis. There are still more options.
“Oil spills happen around the world very, very often, [so hopefully] we can look into [spills] for forecasting as well as […] how we can actually sustainably remove it, and how much […] less energy […] we can use,” Ariya said.
