Quansan Yang, 28, is developing eco-friendlier chips. Micro-electromechanical systems (MEMS) are a type of tiny chip commonly used in sensors like blood pressure monitors and accelerometers. Since they are built to be durable, they often become electronic waste once they have served their purpose. Yang is the first person to find materials and chip architectures that make MEMS chips degradable yet still highly effective. This work has significant implications in the biomedicine field. After patients get MEMS surgical implants that monitor certain biological indicators, they don’t have to go through another surgery to have it removed. Instead, the device will degrade and be absorbed by the body in a few months. The innovation can also be used to produce agricultural and environmental sensors that degrade after their intended use, eliminating the need for manual removal and preventing the devices from polluting the environment.
Yang didn’t stop there. He also invented a new laser-based manufacturing process that enables biodegradable chips to be made efficiently, at a low cost, and with minimum waste. “Many people want to make the fabrication [of chips] sustainable; other people are trying to make degradable electronics, but their fabrication is actually not sustainable,” he says. His work lays the foundation for a more holistic approach: “If we can make the entire life cycle of the devices sustainable, from fabrication to operation to post-processing, that will be very cool.”
Bharath Kannan, 27, found a new way to make quantum computers more powerful by reducing the error rates of their fundamental computing units, known as qubits. Now the company he leads, Atlantic Quantum, is working to build quantum computers that he hopes will advance the fields of encryption, materials science, and machine learning.
What makes quantum computers so powerful is that qubits can exist in multiple states at the same time, allowing for new types of calculations. But qubits are highly unstable and error prone, so companies trying to build quantum computers powerful enough to tackle real-world problems end up at an impasse. Each additional qubit intended for running a calculation may require thousands of additional qubits devoted solely to canceling out its errors. Unfortunately, the problem grows exponentially rather than linearly, so you can’t easily solve it by brute force.
Kannan and Atlantic Quantum have taken a very different approach: building a better qubit. Their so-called fluxonium qubits operate at a much lower frequency than the type used by many other companies, called transmon qubits. At this lower frequency, there is less overlap between signals, which means less interference and fewer errors.
Researchers had known about fluxonium qubits for roughly a decade. But their advantages went unexploited because they were more complex to design and build, and more difficult to control.
Kannan solved those challenges by developing new circuits for building and controlling low-frequency qubits. As an added benefit, he demonstrated that fluxonium-based quantum computers, because they operate more slowly without sacrificing processing speed, can be controlled with simpler hardware than other types of quantum computers require.
When covid-19 started spreading in early 2020, scientists quickly realized that tracking how the virus was mutating would be essential for public health as new strains emerged that put people at greater risk. Yatish Turakhia, then a postdoc at UC Santa Cruz’s Genomics Institute, helped develop a software tool called UShER to track these covid variants by placing them, within minutes of each new sample’s submission, on a family tree of all known SARS-CoV-2 genomes.
The tool, which has been accessible online since 2021, now contains more than 15 million viral sequences, and scientists add to it daily. It helps them and public health officials discover new strains, assign them names, and track their evolution. It also allows them to surveil the virus in real time on a global scale with a high degree of precision.
More recently, the team built another software tool, called RIPPLES, which examines UShER’s extensive family tree structure and investigates whether specific “branches” of variants may be recombinants—genetically distinct hybrid variants.
Before the development of RIPPLES, scientists’ only method of identifying potential recombinants was by remembering mutations they’d spotted in other variants. RIPPLES automates that process, allowing health experts to reconstruct the virus’s evolutionary history.
“Our global understanding of how covid spreads would have been severely compromised without Yatish’s work,” says David Haussler, scientific director of UC Santa Cruz Genomics Institute, who worked with Turakhia on the project. “The product of his algorithm, which nobody else could make, is a global picture of how the virus spread in full genetic detail around the entire globe.”