Human activities release a wide range of pollutants into the air, water, and soil, posing serious threats to both human health and the environment. According to the World Health Organization, air pollution alone is responsible for an estimated 4.2 million deaths annually. In response, scientists are exploring innovative solutions, including a class of materials known as photocatalysts. When exposed to light, these materials trigger chemical reactions that can break down common toxic pollutants, offering a promising method to reduce pollution.

As a researcher in materials science and engineering at the University of Tennessee, I am working alongside my colleagues to develop new photocatalysts. With the help of robots and artificial intelligence, we are aiming to design materials that can efficiently mitigate air pollution.

Photocatalysts work by generating charged carriers when exposed to light. These charged carriers—tiny particles capable of causing chemical reactions—interact with water and oxygen in the environment, producing highly reactive oxygen species. These species, in turn, bond to pollutants, breaking them down into harmless or even useful byproducts.

However, current photocatalysts have some limitations. Many materials only work with high-energy light, such as ultraviolet rays, meaning they cannot utilize lower-energy infrared or visible light. Additionally, the charged particles in the reaction often recombine too quickly, reducing efficiency. Over time, the surface of the photocatalyst can degrade, further affecting its performance.

To address these challenges, our team is developing new photocatalytic materials that can more effectively break down pollutants while remaining non-toxic, ensuring that the solution doesn’t contribute to further pollution.

We are focusing on hybrid perovskites—nanocrystals that are about one-tenth the thickness of a human hair. These nanocrystals, made from both organic and inorganic components, have unique properties that make them ideal candidates for photocatalytic reactions. Their structure allows them to absorb light efficiently and generate a large number of charge carriers.

These nanocrystals also have excellent charge transport abilities, which enable them to harness light energy and drive chemical reactions efficiently. In addition to pollution control, these materials are also used to improve solar panel efficiency and create vibrant displays in LED technology.

With thousands of possible variations of hybrid nanocrystals, we needed an efficient way to identify the most effective materials for breaking down pollutants.

Traditionally, making and testing photocatalytic materials by hand is a time-consuming process, often taking weeks or months. To speed things up, we are using robots that can produce and test up to 100 different materials in just an hour. These liquid-handling robots precisely mix and transfer small amounts of liquid with exceptional accuracy, all under computer control.

We also use machine learning algorithms to guide the process. These algorithms analyze data from experiments and quickly identify patterns and insights that would take humans much longer to uncover. By learning from previous results, the AI refines its approach to the next set of experiments, making the process more efficient and effective.

Through this combination of robotics and AI, we are simplifying the complex process of developing photocatalytic systems. This advanced approach allows us to analyze and interpret these systems more easily, paving the way for new strategies to combat pollution using innovative materials.

By Impact Lab