News Release

Discovering the next generation of catalysts

Peer-Reviewed Publication

University of Copenhagen - Faculty of Science

Energy The use of solar and wind energy must be doubled to meet the world's demand for clean energy over the next 30 years. Catalysts that can ensure the storage of solar and wind energy in fuels and chemicals will therefore play an increasingly important role. The catalysts that are used today are, however, often both expensive and ineffective. Now researchers at the University of Copenhagen and DTU have developed a method that makes it easier to find better and cheaper catalysts, with their results having recently been published in the journal Joule.

The world's energy needs will increase two to three times over the next 30 years - as the world's population goes from approx. 7.3 billion today to approx. 9.7 billion by 2050, according to UN figures.

It is not enough to expand the capacity of solar and wind energy as a substitute for fossil fuels. Both sources satisfy the need for environmental sustainability, but they are unstable due to their reliance on unpredictable weather conditions.

A result of this instability is that catalysts and electrolysis have become increasingly important, in the hope that they are able to ensure a stable energy supply. In addition to this, catalysts are used for many things in the chemical industry; from the conversion of harmful exhaust gases from cars to the conversion of nitrogen from the atmosphere for fertilizers.


Catalyst and Electrolysis: The role of a catalyst is to assist in the conversion of chemical substances in a chemical reaction, and an effective catalyst is able to provide a rapid, inexpensive and efficient pathway for the reaction. Electrolysis is a method of separating a substance by the use of electricity.

Still a long way to go

"There is still a long way to go in the development of catalysts that can be used for e.g. fuel cells, storage of solar and wind energy and new environmentally friendly fuels. The catalysts that exist today are not good enough to ensure a green transition," Professor Jan Rossmeisl at the Department of Chemistry for the University of Copenhagen, points out.

With the aid of two PhD students, Jack K. Pedersen and Thomas A.A. Batchelor, he is looking for "the famous needle in the haystack" among a new generation of catalysts.

But this is no easy task

"It is difficult to find the right alloy of metals for catalysts among infinitely many possibilities - despite today's supercomputers. Finding the best alloys would take a lifetime. We use the so-called high-entropy alloys, which are random mixtures of many different elements, as a starting point and we have developed computer models based on machine learning. In this way, it becomes easier to sort the myriad of combinations of alloys and find those that can solve the problem of converting and storing solar and wind energy efficiently," Professor Jan Rossmeisl emphasizes.


The next generation of catalysts

The chemical industry uses catalysts for processes to run efficiently while remaining environmentally friendly, from the transformation of exhaust gases from cars to the production of fertilizers using nitrogen from the atmosphere. Amongst these chemical processes there are some that do not yet have effective catalysts, and these will require solutions in the near future. For example the conversion of carbon dioxide into useful substances to mitigate climate change, and the reaction between oxygen and hydrogen to form water for use in fuel cells. The role of a catalyst is to aid the conversion of chemical substances in a chemical reaction, and an effective catalyst can do this quickly and with small energy loss. It is a great challenge to predict which material will act as a good catalyst for a chemical reaction, and it is exactly this problem that we propose a solution for with a new class of materials, the so-called high-entropy alloys.

High-entropy alloys are a composed of a mixture of five or more metals, having only recently been used as catalysts. We present the first theoretical study of how to systematically benefit from high-entropy alloys to provide the best alloy candidate that can catalyze a desired chemical reaction.

What makes the high-entropy alloys different from other catalysts is that they have a surface with countless local configurations of different atoms giving rise to as many local chemical environments. Imagine a Rubik's Cube: When it is solved, it consists of six faces each with its own color representing the pure metals. Mix the Rubik's Cube and each face is now composed of many colors. On each face, the six colors can be arranged many different ways. The nine squares represent a local combination of six different metals on the surface of a high-entropy alloy. Some combinations of atoms on the surface will bind the reacting chemical substances weakly, while with others they will bind strongly. For those combinations of atoms where the bond strength is perfect the catalytic activity will be greatest, and these combinations will govern the overall catalytic activity.

By calculating the bond strength of the chemical substances for all configurations of atoms, we can identify the best chemical environments and in what proportion the mixed metals are included at the atomic level. Here, however, we encounter the problem that it would take a lifetime to calculate the bond strengths for all the combinations even with modern quantum mechanical methods. We have solved this problem by calculating the bond strengths of a randomly selected subset of the possible combinations and then used machine learning to calculate the bond strengths for the entire span of combinations in just a few seconds.

When the bond strengths of all local combinations of atoms on the surface are known we are able to tune the ratio of the incorporated metals in order to promote the likelihood that the best bond strengths occur as frequently as possible. This optimal mixing ratio can be calculated and the outcomes are completely new, untested catalysts. The method thus gives us a systematic way of proposing catalysts which only depends on which metals we include. We have used the method to suggest catalysts for the reaction between oxygen and hydrogen forming water but the application is very broad so we are currently working on several other chemical reactions, as well as improving the approximations and assumptions of the method so that we can propose alloys that hopefully exceed the activity of present day catalysts.


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