Over the past decade, research on the development of multi-cellular engineered living systems has produced technologies and capabilities that are now positioned to facilitate a fundamental understanding of disease processes and can help to identify innovative therapeutic strategies. Globally, while many labs are engaged in the development of new and more sophisticated organ models for drug discovery and screening, there is an urgent need to disrupt the way drugs are currently developed. Our vision is to humanize drug development based on a new approach that integrates microphysiological system models of disease and enhanced model control/interrogation, with modern systems biology and systems immunology. This is the focus of Living Machines, one of five threads in the New Engineering Education Transformation (NEET) program to reimagine engineering education at MIT in which sophomores, juniors and seniors, under the guidance of faculty mentors and instructors, learn, discover, build and engineer living systems for broad applications in biotechnology and medical devices. This webinar will share the perspectives of 3 MIT faculty, their research capabilities and interests in which NEET students can participate, and that of several NEET students and what they can or hope to achieve.
Formate Economy and AI-Assisted Catalyst Search Ju Li Battelle Energy Alliance Professor, MIT Department of Nuclear Science & Engineering Professor, MIT Department of Materials Science and Engineering
Carbon efficiency is one of the most pressing problems of carbon dioxide electroreduction today. While there have been studies on anion exchange membrane electrolyzers with carbon dioxide (gas) and bipolar membrane electrolyzers with bicarbonate (aqueous) feedstocks, both suffer from low carbon efficiency. In anion exchange membrane electrolyzers, this is due to carbonate anion crossover, whereas in bipolar membrane electrolyzers, the exsolution of carbon dioxide (gas) from the bicarbonate solution is the culprit. Here, we first elucidate the root cause of the low carbon efficiency of liquid bicarbonate electrolyzers with thermodynamic calculations and then achieve carbon-efficient carbon dioxide electro- reduction by adopting a near-neutral-pH cation exchange membrane, a glass fiber intermediate layer, and carbon dioxide (gas) partial pressure management. We convert highly concentrated bicarbonate solution to solid formate fuel with a yield (carbon efficiency) of greater than 96%. A device test is demonstrated at 100 mA cmÀ2 with a full-cell voltage of 3.1 V for over 200 h. ["A carbon-efficient bicarbonate electrolyzer," Cell Reports Physical Science 4 (2023) 101662]
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Physical neural networks made of analog resistive switching processors are promising platforms for analog computing and for emulating biological synapses. State-of-the-art resistive switches rely on either conductive filament formation or phase change. These processes suffer from poor reproducibility or high energy consumption, respectively. Our work, on one hand, focuses on understanding and controlling the variability of the conductive filament formation in insulating oxide materials. On the other hand, we are innovating alternative synapse designs that rely on a deterministic charge-controlled mechanism, modulated electrochemically in a solid state, and that consists of shuffling the smallest cation, the proton. As typical throughout our research, here, too, we combine experimental synthesis, fabrication, and characterization with first principles-based computational modeling to gain a deep understanding and control of these promising devices.
Guoping Feng