A new Cornell-led study identifies several keys to sustainably managing the influx, with an emphasis on battery chemistry, second-life applications and recycling. The study was led by Cornell Engineering Dean Lynden Archer, Systems field member Fengqi You and Systems Ph.D. student Yanqiu Tao. Read more
Working with the Armenian delegation at COP26, Allison Chatrchyan aims to shape U.N. agriculture policy to reduce greenhouse gas emissions and increase carbon sequestration. Read more
Four Cornell faculty testified to the NYS Assembly Oct. 27 on how firing up once-shuttered carbon-based power plants – to process cryptocurrency – could pause environmental progress. Read more
A team of Cornell students has won a grant from NASA’s University Student Research Challenge for a proposed sensor that can help 3D printers build better, more reliable products. To collect the prize, the team is now crowdfunding a cost-share required by NASA. Read more
Cornell researchers found that by deliberately introducing more defects into a 3D-printed metal alloy, followed by a post-processing treatment, they could create a stronger, more ductile material. Read more
A team of graduate students in food science, mechanical engineering and biological engineering is among the winners of Phase 1 of the NASA Deep Space Food Challenge. Read more
After gazing at Jupiter’s Great Red Spot and its cloudy realm, NASA’s Juno spacecraft has given humanity a 3D, turbulent sense of what lies far below its swirling surface. Read more
Cornell research shows how to make offshore wind farms more efficient in the face of impending rapid expansion, as the U.S. Department of the Interior plans leasing federal waters. Read more
“A Call For Innovation: New York’s Agrifood System,” a report published this past spring by Cornell’s Center for Regional Economic Advancement, is the basis for the topics to be addressed at this year’s Grow-NY Summit, slated to bring food and ag innovators together at the Syracuse Oncenter on Nov. 16-17. Read more
Cornell researchers used machine learning to predict with near-perfect accuracy how genes are transferred between bacteria, an approach that could potentially be used to stop the spread of antibiotic resistance. Read more
