G1: 2022-2023

I entered grad school knowing where I wanted to end up: starting a company using microbial communities engineered for scalable, economically sustainable carbon capture. I'd spent six months in 2020 exploring whether to raise money and find a CTO with a PhD — capital was cheap during zero interest rates, and I probably could have pulled it off — but a Stanford CE course on raising money made it clear that I'd be much happier (and better at) being the CTO once I had the skills and knowledge to make it work long term. I chose grad school to build the scientific foundation myself and earn the credentials to lead the company I wanted to build.

My first year was about getting back into the habit of thinking like a scientist and exposing myself to unknown unknowns. I took classes spanning geology, microbial ecology, data analysis, and environmental modeling. For my props (the GPS term for rotations), I deliberately avoided computational work — coding was a skill I already had. Instead, I worked with Dr. Alex Sessions using GC-FID and GC-TCD to measure how salinity influences microbial methanogenesis and CO₂ production, and with Dr. Victoria Orphan as a member of the PriME collaboration, where I developed a high-throughput BONCAT method to visualize metabolite sharing in Ruegeria pomeroyi communities (apologies for the aggressive use of acronyms, see bottom of page for expansions).

In the spring, things accelerated faster than I'd hoped. I took Richard Murray's Scalable Innovation course, co-taught by industry leaders. We pitched ideas to the class; a team of five ran with mine. Over the quarter, I worked with two undergraduates to interview companies in the algae space — learning from leaders testing biocements, building wastewater treatment plants in Europe, and growing algae for carbon capture in Morocco. The other two group members were graduate students with chemical engineering degrees who helped me build my first techno-economic model: open pond systems with ~$2M capex and ~$0.5M/year opex, achieving ROI in two years through biomaterials, biofuels, carbon credits, and minerals recovered from seawater.

We estimated we'd need land area the size of Hawaii to match global emissions. That number turned out to be wrong (more on that in Year 3), but the exercise pushed me to think at the appropriate scale. I also calculated that a decade of human carbon emissions, compressed into pure diamond, would form a mountain on the scale of Everest (31 km³)* — or roughly twice the water volume of Crater Lake. The wet salted biomass my open ponds will produce would build a new Everest-sized pile every thirty years.** Explore the calculation yourself here. I think the volume of materials we will need to sequester just to keep pace with emissions gives a better intuition for the scale of the problem than the typically used ppm or gigatons.

Horizontal bar chart comparing volumes in cubic kilometers, titled "A decade (and three decades) of carbon vs. published Everest estimates." Two green bars show projected biomass volume from gigaton-scale open ponds (852 km³ over one decade, 2,556 km³ over three decades). Five gray bars show published volume estimates for Mount Everest, ranging from 90 km³ (prominence above surrounding terrain) to 3,879 km³ (full cone to sea level), with intermediate estimates at 400 km³ (above Tibetan Plateau) and 2,400 km³ (sea-level cone). A reference bar shows a decade of human carbon emissions compressed to pure diamond at 31 km³. Three decades of biomass production approximately matches the sea-level cone estimate of Everest.

I spent the summer running experiments for my props and studying for my qualifying exams. In September, I presented a poster at the annual PriME meeting in New York and had the opportunity to discuss my carbon capture ideas with world experts in marine snow, microbial ecology, and biogeochemical cycling. A few days later, I passed my qualifying exam.

*Mountain volumes are notoriously slippery. Published estimates of Everest's volume range from ~90 km³ (just the peak above surrounding terrain) to ~3,900 km³ (a full cone down to sea level) — a 40× spread depending on where you draw the base. The diamond mountain at 31 km³ is the same general order of magnitude as the lower estimates. At this scale human intuition stops distinguishing individual numbers; "Everest" mostly means "a really big pile of rock."

**Using the ~2,400 km³ sea-level cone estimate. With the 90 km³ prominence figure, it's closer to one Everest per year. Either way, we're building Carbon Mountain.

GC-FID: Gas chromatography flame ionization detector (instrument for measuring gas makeup and concentration by combusting the gases)

GC-TCD: Gas chromatography thermal conductivity detector (instrument for measuring gas makeup and concentration by measuring gas conductivity)

PriME: Principles of Microbial Ecology, Simons foundation funded group investigating microbial community dynamics and their biogeochemical cycling impacts using marine snow as a model system.

BONCAT: Bio-Orthogonal Non Canonical Amino Acid Tagging (using specially altered amino acids to visualize anabolic activity)

Check out what I did in my second year at Caltech here.

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