Categories: ArgonneUncategorized

Putting catalysts on track

You could think of Larry Curtiss, Jeff Greeley and their Argonne National Laboratory colleagues as kind of a catalyst screening committee.

Their computer models can quickly sift candidates for these important industrial and environmental materials, identifying the best ones for chemists to try out in time-consuming laboratory tests. They also can help scientists better understand results from such tests. The combination of computation and experiments is helping accelerate the development and improvement of catalysts.

Computational modeling of chemical processes like catalysis has grown in importance in the 33 years since Curtiss joined Argonne.

“The capability has really tremendously increased” as the Department of Energy boosted high-performance computing at Argonne and other labs, says Curtiss, leader of the Theory and Modeling Group and an Argonne Distinguished Fellow. When that power and improved algorithms are combined, “It’s unbelievable compared to 30 years ago how much faster it can get done and the larger systems with more atoms (researchers can model), but also the accuracy with which we can calculate the properties of molecules and materials.

“We can (now) actually make predictions that can help to guide the experimentalists in making catalysts important to reducing the nation’s energy dependency.”

Catalysts increase the rate of a chemical reaction but are not consumed in the process. They’re everywhere in industry, helping to efficiently produce a multitude of materials and chemicals. They’re in your car’s muffler, helping cut pollutants. They enhance reactions that generate power in a fuel cell, help convert grains, cellulose and animal and vegetable oils to biofuels and help clean polluted soil and water. Good catalysts cut the heat and energy needed to cause a reaction. That — and the fact catalysis can reduce the creation of wasteful byproducts in chemical reactions — also makes them easier on the environment than other chemical processes.

Scientists seek several properties in an improved catalyst, Curtiss says. It has to be selective, breaking certain chemical bonds but not others. If the catalyst isn’t selective, it could generate unwanted byproducts that might have to be removed. A good catalyst also has to break those bonds in a way that uses less energy than the process scientists want to replace. The new catalyst also must be stable, so it doesn’t degrade, and be as inexpensive as possible.

The research falls under Argonne’s Center for Nanoscale Materials almost by default, says Greeley, a materials scientist at the center. “In any industrial process, the catalysts are essentially on the order of nanometers. The urgent question is how the size and shape of these particles affect their catalytic properties. In many cases you can obtain significantly improved properties by finding the right size or shape.”

(Visited 801 times, 1 visits today)

Page: 1 2

Thomas R. O'Donnell

Thomas R. O'Donnell is senior science writer at the Krell Institute and a frequent contributor to DEIXIS.

Share
Published by
Thomas R. O'Donnell

Recent Posts

Scaling up metadata

A Los Alamos National Laboratory and Carnegie Mellon University exascale file system helps scientists sort… Read More

May, 2020

Pandemic view – plus privacy

A fellow helps guide an international volunteer effort to develop COVID Watch, a mobile telephone… Read More

April, 2020

Earth’s multimodel future

A PNNL-led team's mission: integrate climate models to reflect human impact on natural resources –… Read More

February, 2020

Beyond the tunnel

Stanford-led team turns to Argonne’s Mira to fine-tune a computational route around aircraft wind-tunnel testing. Read More

January, 2020

Efficiency surge

A DOE CSGF recipient at the University of Texas took on a hurricane-flooding simulation and… Read More

September, 2019

Robot whisperer

A DOE computational science fellow combines biology, technology and more to explore behavior, swarms and… Read More

July, 2019