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Scientists at Stanford and the Department of Energy's SLAC National Accelerator Lab have reengineered the collector, one of the heaviest battery components, so they weigh 80% less and immediately quench any fires that flare up. Using polymers in place of the sheets of copper or aluminum foil made the collector lighter, and increased the energy density of lithium-ion batteries by 16-26%. Reducing battery weight and flammability could also have a big impact on recycling by making the transportation of recycled batteries less expensive.

An internal Blueprint Advisory Committee assigned by Stanford President Marc Tessier-Lavigne is working alongside an external task force to design the new climate and sustainability school announced last May. “This school will provide a home for Stanford students, staff and faculty dedicated to better understanding our planet and the threats it faces, and who are passionate about finding solutions, whether in the form of new technologies or by influencing our own behaviors,” said Kathryn Moler, (seen here on left) who is also a professor of applied physics and of physics.

Automobile and electronics manufacturing have accounted for about 90% of the industrial robots purchased in the U.S. Studies by Stanford researchers examined the impact of AI and robotics in manufacturing, retail banking and nursing homes. Initially the introduction of robots and AI replaced human workers but over time new jobs at least partially offset the losses. It takes time for robots to deliver higher productivity, which can allow companies to expand.

Seventeen high-risk, high-reward concepts based on new, potentially transformative ideas to build a sustainable, affordable, secure energy future have been awarded by Stanford's Precourt Institute for Energy to Stanford faculty and five collaborators at other research institutions. Projects range from assessing health of retired EV batteries, climate and health impacts from battery production and analysis for a U.S. transition to electrified medium and heavy-duty vehicles. See the complete list of projects awarded here.

As part of Stanford's Long-Range Vision, the Ethics, Society and Technology (EST) Hub was launched to help coordinate and amplify the teaching, research and activities on campus at the intersection of ethics, society and technology. Led by political science professors Margaret Levi and Rob Reich, seen here at left, EST has awarded grants supporting ethics in STEM education and courses to ensure students at Stanford consider the social and ethical implications of technology.

In a new study, scientists at SLAC National Accelerator Lab, looked at both the surface and interior of lithium-ion battery electrodes to see how what happens in one affects the other. Their discovery that cracking and chemical changes on the particle's surface varied a lot from place to place and corresponded with areas of microscopic cracking deep inside the particle and sapped its storage capacity, will be applied to other electrode materials focusing on how charging speed affects damage patterns.

A new model developed by Stanford researchers, combines sensor data with computer modeling of the physical processes that degrade lithium-ion battery cells to predict the battery's remaining storage capacity and charge level. This new approach offers a way to predict the true condition of a rechargeable battery in real time and could help pave the way for smaller battery packs.

Researchers have co-designed hardware and software, with hardware just slightly more advanced than what is currently found in autonomous cars, to reconstruct 3D hidden scenes based on the movement of individual particles of light. This technique complements other vision systems used for medical applications and is focused on large scale situations such as self driving cars navigating in fog or heavy rain and satellite imaging of Earth through hazy atmosphere.

Matt Brown, Ph.D. candidate in Mechanical Engineering in Prof. Chris Gerdes' Dynamic Design Lab presented his work on motion planning and control architectures for autonomous vehicles, nonlinear and robust model predictive control, and learning for vehicle control. View the agenda here and the webinar recording here.