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Published on June 20th, 2017 | By: Faye Oney

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‘Group additivity’ approach to Pourbaix diagrams predicts metals’ reactions with water

Published on June 20th, 2017 | By: Faye Oney

[Image above] Credit: Oregon State University; Flickr CC BY-SA 2.0

 

 

Huge structures like bridges and skyscrapers never cease to amaze us. We marvel at the complex design and engineering that went into building enormous structures like the Golden Gate Bridge, the Burj Khalifa, or some of these creations.

 

Many large structures and vehicles, including jet aircraft, are made of steel—and with good reason. It’s structurally sound and lasts a long time. But, like many other metals, over time steel will eventually corrode when it meets water.

 

Last year Japanese airline ANA was forced to ground its fleet of Boeing 787 Dreamliners after corrosion in the engines destroyed their turbine blades. Refurbishing all 100 Rolls-Royce engines that originally cost about $20 million each will take nearly three years.

 

More recently, construction of a bridge in Canada was delayed after the discovery of corrosion in steel components in a shipment from China.

 

In a Perth hospital, stainless steel pipes are corroding due to bacteria in the water system.

 

Steel corrosion was one of the contributing factors to damage in a tower in Naperville, Ill.

 

And corrosion could have been the culprit in this recent deadly explosion that killed three.

 

If engineers had an exact formula for predicting corrosive damage, they could incorporate that into their building and aircraft designs, or any other structures made of metal.

 

That was the thinking behind a collaboration between researchers at Oregon State University (Corvallis, Ore.) and the University of California, Berkeley. They have developed a computational method that could predict what specific reactions occur when combining different combinations of metals with water.

 

Traditionally, researchers use Pourbaix diagrams along with costly experimentation to determine if a combination of metals will corrode under varying conditions. Doug Keszler, distinguished professor of chemistry in Oregon State’s College of Science, explains that when metals dissolve in water they don’t just dissolve into a salt.

 

“In many cases, it [the metal] initially dissolves to form a complex cluster that contains many metal atoms,” Kessler says in an OSU news release. “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.”

 

Keszler and his research team used what they called “group additivity” in creating Pourbaix diagrams that depict stability of clusters that form when metals are dissolved in an aqueous solution. They studied the elements aluminum, gallium, indium, and thallium to quantify their stability as a cluster as a function of pH and concentration in solution.

 

Showing metals’ stable phases in water helps identify corrosion potential. What has been missing in typical Pourbaix diagrams is metal clusters’ reaction to water, Kristin A. Persson, professor of materials science at UC Berkeley and one of the researchers explains in the release. “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,” she adds.

 

“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.”

 

But the benefits of this technique aren’t confined to metals.

 

In addition, “the computational techniques predict selective metal leaching from new ceramic coatings or electrodes at any pH,” he wrote in an email. “Ceramic coatings, for example, may protect pump components in oil, gas, and power industries.”

 

The paper, published in Nature Communications is “Group additivity-Pourbaix diagrams advocate thermodynamically stable nanoscale clusters in aqueous environments” (DOI: 10.1038/ncomms15852).

 


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