{"id":12749,"date":"2024-09-30T22:00:00","date_gmt":"2024-09-30T22:00:00","guid":{"rendered":"https:\/\/modernsciences.org\/staging\/4414\/?p=12749"},"modified":"2024-09-20T03:55:50","modified_gmt":"2024-09-20T03:55:50","slug":"tiny-robots-and-ai-algorithms-could-help-to-craft-material-solutions-for-cleaner-environments","status":"publish","type":"post","link":"https:\/\/modernsciences.org\/staging\/4414\/tiny-robots-and-ai-algorithms-could-help-to-craft-material-solutions-for-cleaner-environments\/","title":{"rendered":"Tiny robots and AI algorithms could help to craft material solutions for cleaner environments"},"content":{"rendered":"\n
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\n \n
\n Air pollution is a global problem, but scientists are investigating new materials that could help clean it up.\n AP Photo\/Sergei Grits<\/a><\/span>\n <\/figcaption>\n <\/figure>\n\n Mahshid Ahmadi<\/a>, University of Tennessee<\/a><\/em><\/span>\n\n

Many human activities release pollutants into the air, water and soil. These harmful chemicals threaten the health of both people and the ecosystem. According to the World Health Organization, air pollution causes an estimated 4.2 million deaths annually<\/a>.<\/p>\n\n

Scientists are looking into solutions, and one potential avenue is a class of materials called photocatalysts<\/a>. When triggered by light, these materials undergo chemical reactions that initial studies have shown can break down common toxic pollutants<\/a>. <\/p>\n\n

I am a materials science and engineering researcher<\/a> at the University of Tennessee. With the help of robots and artificial intelligence, my colleagues and I are making and testing new photocatalysts with the goal of mitigating air pollution. <\/p>\n\n

Breaking down pollutants<\/h2>\n\n

The photocatalysts work by generating charged carriers<\/a> in the presence of light. These charged carriers are tiny particles that can move around and cause chemical reactions. When they come into contact with water and oxygen in the environment, they produce substances called reactive oxygen species<\/a>. These highly active reactive oxygen species can bond to parts of the pollutants and then either decompose the pollutants or turn them into harmless \u2013 or even useful \u2013 products. <\/p>\n\n

\n \"A<\/a>\n
\n To facilitate the photocatalytic reaction, researchers in the Ahmadi lab put plates of perovskite nanocrystals and pollutants under bright light to see whether the reaction breaks down the pollutants.<\/span>\n Astita Dubey<\/span><\/span>\n <\/figcaption>\n <\/figure>\n\n

But some materials used in the photocatalytic process have limitations. For example, they can\u2019t start the reaction unless the light has enough energy \u2013 infrared rays with lower energy light, or visible light<\/a>, won\u2019t trigger the reaction. <\/p>\n\n

Another problem is that the charged particles involved in the reaction can recombine too quickly, which means they join back together before finishing the job. In these cases, the pollutants either do not decompose completely or the process takes a long time to accomplish.<\/p>\n\n

Additionally, the surface of these photocatalysts can sometimes change during or after the photocatalytic reaction, which affects how they work and how efficient they are.<\/p>\n\n

To overcome these limitations, scientists on my team are trying to develop new photocatalytic materials that work efficiently to break down pollutants. We also focus on making sure these materials are nontoxic so that our pollution-cleaning materials aren\u2019t causing further pollution.<\/p>\n\n

\n \"A<\/a>\n
\n This plate from the Ahmadi lab is used while testing how perovskite nanocrystals and light break down pollutants, like the blue dye shown. The light blue color indicates partial degradation, while transparent water signifies complete degradation.<\/span>\n Astita Dubey<\/span><\/span>\n <\/figcaption>\n <\/figure>\n\n

Teeny tiny crystals<\/h2>\n\n

Scientists on my team use automated experimentation and artificial intelligence<\/a> to figure out which photocatalytic materials could be the best candidates to quickly break down pollutants. We\u2019re making and testing materials called hybrid perovskites<\/a>, which are tiny crystals \u2013 they\u2019re about a 10th the thickness of a strand of hair.<\/p>\n\n

These nanocrystals are made of a blend of organic (carbon-based) and inorganic (non-carbon-based) components. <\/p>\n\n

They have a few unique qualities, like their excellent light-absorbing properties, which come from how they\u2019re structured at the atomic level. They\u2019re tiny, but mighty. Optically, they\u2019re amazing too \u2013 they interact with light in fascinating ways to generate a large number of tiny charge carriers and trigger photocatalytic reactions. <\/p>\n\n

These materials efficiently transport electrical charges, which allows them to transport light energy and drive the chemical reactions. They\u2019re also used<\/a> to make solar panels more efficient and in LED lights, which create the vibrant displays you see on TV screens. <\/p>\n\n

There are thousands of potential types of hybrid nanocrystals. So, my team wanted to figure out how to make and test as many as we can quickly, to see which are the best candidates for cleaning up toxic pollutants. <\/p>\n\n

Bringing in robots<\/h2>\n\n

Instead of making and testing samples by hand \u2013 which takes weeks or months \u2013 we\u2019re using smart robots<\/a>, which can produce and test at least 100 different materials within an hour. These small liquid-handling robots can precisely move, mix and transfer tiny amounts of liquid from one place to another. They\u2019re controlled by a computer that guides their acceleration and accuracy.<\/p>\n\n

\n \"A<\/a>\n
\n The Opentrons pipetting robot helps Astita Dubey, a visiting scientist working with the Ahmadi lab, synthesize materials and treat them with organic pollutants to test whether they can break down the pollutants.<\/span>\n Jordan Marshall<\/span><\/span>\n <\/figcaption>\n <\/figure>\n\n

We also use machine learning<\/a> to guide this process. Machine learning algorithms can analyze test data quickly and then learn from that data for the next set of experiments executed by the robots. These machine learning algorithms can quickly identify patterns and insights in collected data that would normally take much longer for a human eye to catch. <\/p>\n\n

Our approach aims to simplify and better understand complex photocatalytic systems, helping to create new strategies and materials. By using automated experimentation guided by machine learning, we can now make these systems easier to analyze and interpret, overcoming challenges that were difficult with traditional methods.\"The<\/p>\n\n

Mahshid Ahmadi<\/a>, Assistant Professor of Materials Science and Engineering, University of Tennessee<\/a><\/em><\/span><\/p>\n\n

This article is republished from The Conversation<\/a> under a Creative Commons license. Read the original article<\/a>.<\/p>\n<\/div>\n\n\n\n

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