Universal fragment descriptor predicts materials properties
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ity in pore size allows electrolytes and gas molecules to diffuse through the MOF during catalysis. Thirdly, growing the NiFe-MOF directly on the electrode gives researchers more control over the final MOF-electrode architecture. This is the first demonstration of a 2D MOF being fabricated directly on a substrate. Lastly, this bottom-up approach is much simpler than other methods for creating 2D MOFs. “This synthetic approach is facile, universal, and adaptable for a range of MOFs and substrates,” says Zongping Shao of Curtin University in Perth, Australia, who was not connected with the publication. All of these factors combine to give this 2D NiFe-MOF its versatility and
(a) Optical and (b) scanning electron microscope image of the 2D nickel/iron metal–organic framework. Courtesy of Nature Publishing Group.
high performance. Electrocatalytic water splitting combines an oxygen evolution reaction at the anode with a hydrogen evolution reaction at the cathode. Zhao and Chen’s 2D NiFe-MOF performs both of these reactions efficiently, significantly mitigating the energy losses caused by the slow kinetics of these reactions. Furthermore, an electrochemical cell with the 2D NiFe-MOF as both the cathode and anode showed excellent catalytic activity, producing a current density of 10 mA cm–2 at a voltage of 1.55 V. This activity is higher than that of most bifunctional catalysts, and is close to the activity demonstrated in standard precious-metalbased catalysts that are used as a benchmark for performance. These results are only in their infancy, but researchers are excited about what this could mean for future MOF applications. “This could open up a new avenue for further tailoring and utilizing MOFs as high-performance electrocatalysts,” Shao says. He would also like to see a more thorough understanding of how the substrate might affect the catalytic activity of the NiFe-MOF. Looking forward, Zhao says his group hopes to “expand MOF applications beyond water splitting” potentially addressing “challenging problems such as electro-reduction of carbon dioxide to generate liquid fuels.” Lauren Borja
by applying machine learning techniques to such data, as reported in a recent issue of Nature Communications (doi:10.1038/ ncomms15679). At the heart of their approach is what they call a “universal fragment descriptor,” which is essentially a labeled graphical representation of the unit cell of an inorganic material. For a given crystal, all the nearest neighbors are identified and a graph is constructed with atoms as the nodes and the bonds as edges. This infinite graph is then broken down to the simplest fragments that capture the local topology in a matrix. Combined with several chemical and physical properties of each atom, these graphs form propertylabeled materials fragments (PLMFs) or a
“colored graph” in graph theory terminology. The schematic for this construction is given in the Figure. “Methodologically, we could apply this technique to any material, even amorphous solids,” Olexandr Isayev of UNC, one of the resea
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