2026 Research Grants

Synthesis of Biodegradable Plastics by Engineered Enzymes Enabled by Machine Learning

PI: Frances Arnold
Research Team: Ethan Quinn
Division of Chemistry and Chemical Engineering and Division of Biology and Biological Engineering
Climate Science Initiative and Ecology and Biosphere Engineering Initiative

Sustainable large-scale synthesis of useful biodegradable plastics can be facilitated by evolved enzymes created through machine learning guided directed evolution.

Accelerated Discovery of Earth-Abundant Zintl-Compound Semiconductors for High-Voltage, High-Radiative-Efficiency Solar Cells with >30% Efficiency

PI: Harry Atwater
Research Team: Mina Mandic and Amelyn Phang
Division of Engineering and Applied Science
Sunlight to Everything Initiative

We propose an accelerated discovery program to synthesize and characterize a new class of solar energy materials, Zintl-compound semiconductors, for high-efficiency photovoltaic applications through combinatorial thin-film synthesis, and closed-loop characterization, targeting high radiative efficiency and tunable bandgaps for single- and tandem-junction solar cell applications.

Synthetic routes to iridoids for invasive ant control

PIs: Sarah E. Reisman and Joseph Parker
Research Team: Joshua Belfield and Hayley Smihula
Division of Chemistry and Chemical Engineering and Division of Biology and Biological Engineering
Climate Science Initiative and Ecology and Biosphere Engineering Initiative

This proposal will pioneer a new approach for the biological control of invasive ants through the synthesis and testing of artificial trail pheromones that disrupt foraging.

Heavy metal transport in the wake of urban megafires: Building a dynamical framework to assess, mitigate, and adapt to an emerging climate threat

PIs: Francois Tissot and Christian Frankenberg
Research Team: Merritt McDowell
Division of Geology and Planetary Science
Climate Science Initiative

In the context of growing urban megafires threat, we propose (i) acquiring heavy metal concentration data on fine dust and coarse ash from the Eaton Fire, and (ii) combining them with dynamical modeling to generate a predictive open-source model of atmospheric particle transport that would improve community-wide emergency response measures.