Researchers at Oregon State University (Corvallis, Oregon) (OSU) and the University of California, Berkeley (Berkeley, California) (UC Berkeley) have developed a new computational method that combines two techniques to make faster, cheaper, and more effective predictions on how metals react with water.
The findings could have a wide range of applications, the researchers explain, including in the design of bridges and aircraft engines—both of which are susceptible to corrosion.
Every metal except precious metals like gold and silver reacts with water, says Doug Keszler, a chemistry professor at OSU.
“We’d like to predict the specific reactions of metals and combinations of metals with water and what the products of those reactions are, by computational methods first as opposed to determining them experimentally,” Keszler says.
Traditionally, Keszler notes, the chemical assumption when looking at metals dissolved in water has been that a metal dissolves to form a simple salt. That’s not always what happens, however.
“In many cases, it initially dissolves to form a complex cluster that contains many metal atoms,” Keszler says. “We can now predict the types of clusters that exist in solution, therefore furthering the understanding of metal dissolution from a computational point of view.”
Studying aqueous metal oxide and hydroxide clusters from Group 13 elements—aluminum, gallium, indium, and thallium—scientists coupled quantum mechanical calculations with a “group additivity” approach to create Pourbaix diagrams, the standard for describing dissolved metal species in water.
“Applying this new approach, we arrive at a quantitative evaluation of cluster stability as a function of pH and concentration,” says Paul Ha-Yeon Cheong, an associate chemistry professor at OSU and co-author of a
study on the method.
Understanding clusters is critical because of the role they play in chemical processes ranging from biomineralization to the solution-deposition of thin films for electronics applications, the researchers explain. Moreover, characterizing corrosion stems from being able to depict metals’ stable phases in water.
“If you’re designing a new steel for a bridge, for example, you’d like to include the potential for corrosion in a computational design process,” Keszler says. “Or if you have a new metal for an aircraft engine, you’d like to be able to determine if it’s going to corrode.”
In 2016, the researchers note that a Japanese airline had to refurbish numerous Rolls-Royce engines on its fleet of Boeing 787 Dreamliners after a series of engine failures caused by the corrosion and cracking of turbine blades.
“Most Pourbaix diagrams do not include these metal clusters, and hence our understanding of metal dissolution and reaction with water has been lacking,” says Kristin A. Persson, a professor of materials science at UC Berkeley and co-author of the study1. “We have now uncovered a fast and accurate formalism for simulating these clusters in the computer, which will transform our abilities to predict how metals react in water.”
The U.S. National Science Foundation (Arlington, Virginia) partially funded the research.
Reference
1 L.A. Wills, et al., “Group Additivity-Pourbaix Diagrams Advocate Thermodynamically Stable Nanoscale Clusters in Aqueous Environments,” Nature Communications 8, 15852 (2017).