Designing Electronic Noses with MOFs & Molecular Simulations

When: Wednesday, June 23, 2021, 3:00 PM - 4:00 PM
Where: Zoom

Speaker: Prof. Christopher Wilmer (University of Pittsburgh)

While electronic ears and eyes (better known as microphones and cameras) work extremely well and are by some metrics better than their biological counterparts, electronic noses come nowhere near the capabilities of biological noses. Biological noses often have hundreds, if not thousands, of unique olfactory receptors, which may be an important factor in their vastly superior performance to existing electronic noses. Here, metal-organic frameworks (MOFs), which are crystalline porous materials with high internal surface areas, may help pave the road to a future where robots can smell as well as humans.

Abstract: While electronic ears and eyes (better known as microphones and cameras) work extremely well and are by some metrics better than their biological counterparts, electronic noses come nowhere near the capabilities of biological noses. One the challenging aspects of electronic nose design is that one needs a large array of chemically distinct materials to act as artificial olfactory receptors. However, it has been a challenge over the last thirty years to make arrays beyond 20 or so materials, likely due to the difficulty of finding a 21st material whose gas adsorption behaviour is not just some linear combination of the first 20. Biological noses often have hundreds, if not thousands, of unique olfactory receptors, which may be an important factor in their vastly superior performance to existing electronic noses.

Here, metal-organic frameworks (MOFs), which are crystalline porous materials with high internal surface areas, may help pave the road to a future where robots can smell as well as humans. Over the last two decades, over 70,000 unique MOF structures have been reported in the literature, with richly varying pore geometries and surface chemistries. This rich materials space is fertile ground for building large but chemically complementary gas sensing arrays. In recent years, our lab has made use of high-throughput molecular modelling to search the large space of MOF-based gas sensing arrays to find optimal combinations of MOFs for different gas sensing applications. Although this research is still in its early stage, we demonstrate the large impact of intelligent MOF selection for array building versus random (i.e., "trial and error") selection on device performance. We hope this will create a practical path towards building larger arrays for designing electronic noses in the future.