Quick and easy way to identify best MOF for the job

In recent years, a class of highly absorbent, nanoporous materials called metal-organic frameworks (MOFs) have emerged as a promising material for carbon capture in power plants. But finding the best MOF for the job can be quite a challenge.

"People are really excited about these materials because we can make a huge variety and really tune them," said Randall Snurr, professor of chemical and biological engineering at Northwestern University. "But there's a flip side to that. If you have an application in mind, there are thousands of existing MOFs and millions of potential MOFs you could make. How do you find the best one for a given application?"

Snurr and his group have now discovered a quick way to identify top candidates for carbon capture – using just 1% of the computational effort that was previously required. By applying a genetic algorithm, they have been able to search rapidly through a database of 55,000 MOFs.

"In the past, we had to evaluate all 55,000 candidates one at a time," Snurr said. "We just marched through them and calculated all of their properties. This genetic algorithm allows you to avoid that."

One of the identified top candidates, a variant of NOTT-101, has a higher capacity for carbon dioxide (CO₂) than any MOF reported in the scientific literature for the relevant conditions. This information could lead to new designs for cleaner power plants.

"The percentage of carbon dioxide that the MOF can absorb depends on the process," Snurr said. "The US Department of Energy (DOE) target is to remove 90% of carbon dioxide from a power plant; it's likely that a process using this material could meet that target."

Supported by the DOE, the research is described in a paper in Science Advances. Yongchul Chung and Diego Gomez-Gualdron, former postdoctoral fellows in Snurr's laboratory, were the paper's co-first authors. Northwestern chemistry professors J. Fraser Stoddart (recent recipient of the Nobel Prize in Chemistry), Joseph Hupp and Omar Farha contributed to the work, as did Fengqi You, former professor of chemical and biological engineering at Northwestern.

With their nanoscopic pores and incredibly high surface areas, MOFs are excellent materials for gas storage, able to hold remarkably high volumes of gas. Whereas individual MOF crystals might be the size of a grain of salt, for example, their internal surface area, if unfolded, could cover an entire football field.

Snurr's previous work has explored how to use MOFs to capture carbon from existing power plants during the post-combustion process. CO₂ accounts for 10–15% of power plant emissions; the rest is mainly nitrogen and water vapor. Snurr and Hupp have designed a MOF that can sort these gases to capture CO₂ before it enters the atmosphere.

This capturing becomes a lot easier after a little chemical processing. Chemically processing the fuel before it enters the power plant can turn it into CO₂ and hydrogen. After the MOF captures the CO₂, the hydrogen is burned, with water as the only by-product. This extra chemical processing step would need to be built into new power plants as a pre-combustion process.

"In places like China, where they are still building a lot of power plants," Snurr said, "this would make a lot of sense."

An optimization technique that mimics natural selection, the genetic algorithm takes a random population of candidate solutions and evolves them toward better solutions through mutation, crossover and selection. Snurr said this technique has been applied to material screening in the past, but not to the search for top candidates for the pre-combustion process, which he describes as a ‘new challenge’.

To tackle carbon capture in pre-combustion, the genetic algorithm identified NOTT-101 as a top candidate. (The material is named after Nottingham, where this particular MOF was first discovered.) Hupp and Farha created the NOTT-101 variant and tested it in the laboratory. Out of all of the MOFs that have been evaluated for pre-combustion, this material had the highest capacity for capturing carbon, as well as good selectivity for grabbing CO₂ over hydrogen.

"Initially, I wasn't sure how well this algorithm would work," Snurr said. "But using just 1% of the usual computational effort is a significant improvement in speed. It's very exciting."

This story is adapted from material from Northwestern University’s McCormick School of Engineering, with editorial changes made by Materials Today. The views expressed in this article do not necessarily represent those of Elsevier. Link to original source.