Data-driven approach accelerates single-atom catalyst development for water purification

All humans need clean water to live. However, purifying water can be energy-intensive, and therefore there is great interest in improving this process. Researchers at Tohoku University have recently reported a strategy using data-driven predictions coupled with precise synthesis to accelerate the development of single-atom catalysts (SACs) for more robust and efficient water purification.

The study is published in Angewandte Chemie International Edition.

SACs are one of the most crucial catalysts. They play a pivotal role in enhancing efficiency in diverse applications including chemical industries, energy conversion, and environmental processes. For water purification in particular, SACs can overcome the limitations of traditional heterogeneous catalysts such as kinetics, catalytic selectivity, and stability—a promising approach to the advancement of efficient and sustainable water purification technologies.

However, the development of SACs frequently employs time-consuming trial-and-error methods, and the typical synthesis methods often lack a high level of control. To avoid a process that essentially involves taking shots in the dark, researchers took a data-driven approach where they rapidly and accurately predicted which SACs would have the best performance before even starting to make them. They compared 43 metals-N₄ structures comprising transition and main group metal elements using a hard-template method.

Following this strategy, they determined that the best candidate was a well-designed Fe-SAC with a high loading of Fe-pyridine-N₄ sites (~3.83 wt%) and a highly mesoporous structure. It successfully exhibited ultra-high decontamination performance (rate constant of 100.97 min^-1 g^-2).

“The optimized Fe-SAC can also continuously operate for 100 hours,” remarks Associate Professor Hao Li of WPI-AIMR, “To our knowledge, this represents one of the best performances of wastewater purification on Fenton-like catalysts—which are reagents used for water purification—reported so far.”

Density functional theory calculations revealed the underlying mechanism: The SAC reduced the energy barrier of the rate-determining step, which is intermediate O* formation. This resulted in the highly selective generation of singlet oxygen, which has been shown to break down pollutants to help purify water.

To make sure the data-driven prediction had accurately selected this “best” candidate, the research team looked at the N₄ structures of five other metals (Fe, Co, Ni, Cu, and Mn) with different theoretical activities. They confirmed that Fe-SAC truly exhibited the most excellent Fenton-like performance among the five selected SACs, agreeing well with the data-driven prediction.

The close integration of a data-driven method with a precise synthesis strategy provides a novel paradigm for the rapid development of high-performance catalysts for environmental fields, and other fields that involve sustainable energy and catalysis. Moving forward, the researchers aim to develop an efficient and user-friendly workflow for the rapid and effective design of catalysts.

Those interested in incorporating the method into their own work can view the experimental data and computational structures in the Digital Catalysis Platform (DigCat): the largest experimental catalysis database reported to date, developed by the Hao Li Lab.