Discover how small robots and AI and their work together will be used in a unique way to produce new photocatalytic materials that are capable of decomposing various harmful pollutions present in the air, water, or soil to make an environmentally friendly environment.
Harnessing the Power of Photocatalysts
One of the greatest environmental challenges on a global scale is air pollution, responsible for more than 4.2 million deaths across the planet every year according to data from the World Health Organization. Scientists are working on it, and one promising area is photocatalysts – materials that can react to light and process common toxic pollutants with such reactions.
Photocatalysts make charged carrier atoms, or particles, that can move from spot to site and set off chemical reactions. When they mix with water and oxygen, they generate reactive oxygen species that can then stick to pollutants and break them down or convert them into benign or useful products. However current photocatalytic materials require high-energy light to start the reaction or suffer from rapid recombination of the charged particles, which impedes the process.
The Dynamic Duo: Robots and AI
To work around these limitations, researchers at the University of Tennessee are turning to the combined power of automation and artificial intelligence to discover novel photocatalytic materials that are far more effective.
They rely on miniaturized, high-throughput liquid-handling robots to pump out many different advanced hybrid perovskite materials quickly — crystal flakes the size of a red blood cell and composed of mixtures of inexpensive small organic molecules and inorganic elements that have remarkable light-absorbing and charge-conducting properties.
With the automation of this experimentation process, researchers can quickly produce and test these materials in a fraction of the time it would take to do them manually, which could often require weeks or months. This is posed to generate around 100 different materials an hour, which can be then tested by the robots using machine learning algorithms that speedily make sense of this data and inform the next set of tests.
Machine learning algorithms such as the use of convolutional deep networks can determine patterns and insights that might be difficult for humans to spot which in turn allows researchers to streamline and make sense of intricate photocatalytic systems. This method is useful for making improved strategies and several resources on the way to clean off toxic impurities.
Conclusion
Now team of researchers led by the National Central University in Taiwan have developed this strategy in real life — using tiny robots powered with AI algorithms to pinpoint sources of air pollution. Taking this step closer to the dream of creating a cleaner environment for us all, cooler and better photocatalytic materials can be developed with advantageous futuristic properties by these researchers on their way fast lane inclusive implementation and huge penetration of solutions that help break down harmful pollutants.
