Skip to Content

From Leftovers to Energy

Scientists develop microbes that convert food scraps into energy.
June 18, 2007

Nestled in the farmland surrounding the University of California campus in Davis (UC Davis) is a set of giant vats filled with hungry microbes. The bugs are devouring cafeteria leftovers and lawn clippings and converting them into biogas–mostly methane–that can be burned to generate electricity or compressed into liquid to power specialized vehicles. However, scientists know little about the gas-producing microbes living within the reactors. But a new project to sequence the genomes of the microbes could change that, allowing researchers to figure out how the bugs perform their digestive tasks and suggesting new ways to make more-productive bioreactors.

“Sequencing these organisms will give us a better idea of who the players are so we can better control the conditions or improve the design to further improve conversion of waste into biogas,” says Ruihong Zhang, the UC Davis bioengineer who developed the system.

Similar bioreactors, known as anaerobic digesters, are commonly used at wastewater treatment plants. Zhang’s bioreactor, however, is different because it’s designed to work on solids, such as food and yard waste. It works 30 to 50 percent faster than conventional systems and presents a promising new way to cut back on landfill waste, producing clean burning gas in the process. (Natural gas, which is primarily made up of methane, releases fewer toxic compounds into the air than gasoline or diesel fuels.)

An industrial-sized demonstration unit has been running at UC Davis since last October, converting eight tons of restaurant waste, cafeteria scraps, and lawn clippings into 300,000 to 600,000 liters of biogas a day–enough to power approximately 80 homes. (In Davis, the gas is used for electricity and powers the nearby wastewater treatment plant.)

Still, scientists know little about the microbes that convert the waste into gas. “In nature, the microbes that carry out degradation of organic waste and generation of methane exist in a very complex anaerobic community, and individual isolates from the community are hard to grow,” says Jim Bristow, head of the community sequencing program at the Department of Energy’s Joint Genome Institute, in Walnut Creek, CA. But in the past two years, faster and cheaper gene-sequencing methods have offered microbiologists a new tool for studying microbial communities. Scientists can isolate DNA from a drop of bioreactor sludge and generate the gene sequence for the entire microbial community. The Joint Genome Institute will use this approach to sequence the genomes of the microbes in Zhang’s digester next year.

The results should shed light on the types of microbes living in the bioreactor and the types of genes that predominate. Researchers will also be able to examine how the community changes under different temperatures and acidities, which can drastically alter the efficiency of the system. “We want to compare what kind of microbes are there at different conditions and try to figure out why one [set of conditions] works better than the other,” says Martin Wu, a geneticist at UC Davis who will lead the genomics part of the project.

Zhang has partnered with Onsight Biosystems, a Davis-based startup, to commercialize the system. She says the technology has garnered interest from food producers and municipalities.

Keep Reading

Most Popular

Large language models can do jaw-dropping things. But nobody knows exactly why.

And that's a problem. Figuring it out is one of the biggest scientific puzzles of our time and a crucial step towards controlling more powerful future models.

OpenAI teases an amazing new generative video model called Sora

The firm is sharing Sora with a small group of safety testers but the rest of us will have to wait to learn more.

Google’s Gemini is now in everything. Here’s how you can try it out.

Gmail, Docs, and more will now come with Gemini baked in. But Europeans will have to wait before they can download the app.

This baby with a head camera helped teach an AI how kids learn language

A neural network trained on the experiences of a single young child managed to learn one of the core components of language: how to match words to the objects they represent.

Stay connected

Illustration by Rose Wong

Get the latest updates from
MIT Technology Review

Discover special offers, top stories, upcoming events, and more.

Thank you for submitting your email!

Explore more newsletters

It looks like something went wrong.

We’re having trouble saving your preferences. Try refreshing this page and updating them one more time. If you continue to get this message, reach out to us at customer-service@technologyreview.com with a list of newsletters you’d like to receive.