Imagine a global society powered by clean and abundant renewable energy. To make this transition possible, society must transform both the way it produces and consumes energy. Our research thus targets both required transformations: solar energy conversion to boost clean fuel production, and electrochemical energy storage to reprogram consumption.
We have identified fundamental scientific problems in each research thrust, and focused on the specific knowledge gaps that impede advancement. In both areas, materials heterogeneity prevents us from understanding fundamental processes whose elucidation is vital to progress, including: Li-ion diffusion in the solid state, charge transport, and interfacial charge transfer. To close this critical knowledge gap, we develop unique light microscopy tools that allow us to study how the composition, morphology, crystal facets, and surface chemistry of electrode materials influence energy conversion and storage performance. Our single particle-level approach simplifies interpretation of experimental data, pin-points how and why nanoscale materials function or fail, and reveals fundamental knowledge that could not be obtained with conventional measurements—after all, nanoparticles appear as a faint speck in a standard light microscope! The structure-property relations we discover lead to new design principles for superior functional materials.
- Electrochemical energy storage: Toward fast-charging electrode materials. (Supported by NSF).
Solar energy conversion: Toward hot carrier solar energy conversion systems (Supported by Air Force and Dept. of Energy)
- Single molecule photocatalysis.
Nanomaterial samples are generally heterogeneous. That is, the chemical composition, size, and shape varies both at the inter- and intra-particle levels. Conventional analytical techniques measure the average behavior of many, individual nanomaterials and therefore cannot resolve single nanoparticle behavior. What if a minor, “magic” population contributes to all of the sample’s function? Our group circumvents ensemble averaging by using sub-particle-level characterization techniques.