Researchers from ORNL found that copper nano-particles supported on carbon nano-spikes can electrochemically catalyse the transformation of carbon dioxide (CO2) to ethanol. These are great news, because because CO2 is a greenhouse gas and ethanol is a fuel, which means we are transforming waste to fuel! Industrially, the process could be used for the storage of excess electricity from renewable energy sources. The CO2 is dissolved in water and the catalyst electrochemically transforms it directly to ethanol. The yield is 63%!
Glass can be formed naturally; for example in volcanoes or when lightning strikes a sandy beach and it has been produced by humans for thousands of years. Glasses can take on many different appearances, colors, and properties. Thus, it may be surprising that our understanding of the exact structure of glass is not complete, even after such a long time. Very often, you will hear glasses described as amorphous. This word is derived from Greek and means without shape. So we have a rough idea that glass is somehow shapeless. In the following we will see, how we can investigate the atomic arrangement in glass using modern techniques.
The design of novel materials plays a key role in the advancement of technologies in any application field. It is therefore crucial that the materials research is pursued with optimal effectiveness and efficiency. Modern computational materials design in synergy with concepts from big data processing and -storage can largely contribute to meet this requirement. For example, the systematic investigation of a large set of bulk materials can be realized fast and cost effective with high-throughput (HT) electronic structure methods.
The general procedure for a HT-approach is to compute the properties of interest of a large set of possible materials. The information is then ideally stored in a searchable database. The last step is the materials search and selection. With statistical and graphical means, the properties of a large set of materials can be visualized. In fig. 1, the formation energy of a binary alloy (FePt) is shown as an example.
Fig. 1: The alloy formation energy of Fe-Pt alloys as function of the composition. Many different structures have been screened (red crosses) and the most stable structures lay on the blue line (convex hull). Data taken from the AFLOWLIB repository.
What are they?
Flexible whole cell biosensor is an appealing field. Whole-cells are a natural factory of biocatalysts. Usually, the method for acquiring isolated biocatalysts, like the enzymes, requires a process of separation and purification from the raw whole-cell strain or tissue, which is tedious, time and resource consuming. Furthermore, added advantage of whole cell biosensor over enzyme biosensor are that the enzyme sometimes requires cofactors and coenzymes, to carry out a complete reaction or to recognize a substrate, which implies a recycling process to attain the required cofactor, then the need for further separation and purification steps. Whole-cells contain a complete metabolic aggregate of enzymes, cofactors and coenzymes. Analog processes can be found in tissues. The requirements related to maintenance and cost for culturing microorganisms are below from those of tissue cultures.
To compare and predict the performance of heterogeneous catalysts, it is important to have a standard way to describe the catalytic activity. The catalytic activity describes how good a catalyst is working for a given reaction. When we have a normalized measure for the catalytic activity, we can compare different catalysts and find out which is the best one. But this means, that we have to do a lot of experiments and try and fail until we found a good catalyst. It would be better, if we could predict whether a material will be a good catalysts for a given reaction or not.
The catalytic activity is dependent on the physical and chemical properties of the catalyst material, and of course the reaction conditions, i.e. temperature, pressure and the reactant concentrations. So, if we want to predict the catalytic activity of a material, we have to have information about the material's properties. As we will see in the following, a few parameters can be sufficient to get an idea on the performance of a material as a catalyst for a given reaction. To predict the catalyst quality we can use the Sabatier principle.
CO2 is a greenhouse gas and to reduce its concentration in the atmosphere, there are three possibilities: We can minimize its production, we can store it and we can use it to make other chemical compounds. Scientists are trying realize all three possibilities to reduce the CO2 concentration in the air. There are several challenges to face, such as to make the 400ppm CO2 content of the atmosphere into usable quantities of CO2 in proper density.
Of course, it would be nice to transform CO2 into "value-added" chemicals, which are for instance methanol, fuels or methane. There are several catalysts that can transform CO2 into these chemicals and research is done to optimize these catalysts for an efficient industrial use.