Another Step Towards Programmable Synthetic Materials

Another Step Towards Programmable Synthetic Materials

Today, as we all know information is made up of zeroes and ones but did you know that one day artificial molecules could form the information unit of a new type of computer or be the basis for programmable substances. The information would be encoded in the spatial arrangement of the individual atoms.

Yes, researchers from the University of California, Berkeley, and Ruhr-Universität Bochum (RUB) have made an advancement towards this vision by showing that atom probe tomography (APT) can be used to read a complex spatial arrangement of metal ions in multivariate metal-organic frameworks.

What are metal-organic frameworks(MOF)?
Metal-organic frameworks are a class of compounds consisting of metal ions or clusters coordinated to organic ligands to form one-, two-, or three-dimensional structures. They are a subclass of coordination polymers, with the special feature that they are often porous.

Until now there was no method to read the metal sequence of MOFs but this team of researchers has achieved this using APT. For encoding information in MOFs, reading was the biggest challenge which was targeted in this research.

There are several MOFs which exist but the team chose The researchers chose MOF-74, made by the Yaghi group in 2005, as an object of interest. They designed the MOFs with mixed combinations of cobalt, cadmium, lead, and manganese, and then decrypted their spatial structure using APT.

The programmable synthetic materials can have various uses like drug release, capturing CO2 and converting it into useful materials, different medications etc.

“In the long term, such structures with programmed atomic sequences can completely change our way of thinking about material synthesis,” write the authors. “The synthetic world could reach a whole new level of precision and sophistication that has previously been reserved for biology.”

Journal Reference
Zhe Ji, Tong Li, Omar M. Yaghi. Sequencing of metals in multivariate metal-organic frameworks. Science, 2020 DOI:

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