The American Institute for Medical and Biological Engineering recognized Brian Pfleger for his pioneering contribution to the metabolic engineering of microorganisms for sustainable chemical production and outstanding leadership to the biotechnology community.

Mike Wagner and Allison Mahvi, both assistant professors of mechanical engineering, will serve as co-directors of UW–Madison's Solar Energy Laboratory, which has been conducting research on solar energy since 1954.

Developed by a multi-institutional team led by University of Wisconsin-Madison engineers, a new methodology for evaluating diffusion welds offers a unique way for manufacturers, regulators and vendors to “view” the material bonds integral to the exchanger to ensure they are strong.

This May, WiscWind students will travel to California Polytechnic State University in hopes of bringing home another victory. Watch now to learn how these students are blowing away the competition.

Lithium-ion batteries enable cleaner energy and transportation systems, but their growing roles powering vehicles and balancing the grid presents a two-pronged problem: meeting the demand for critical minerals

Building on previous work using RCF to deconstruct poplar, Great Lakes Bioenergy Research scientists evaluated six solvents in pure form and in varying mixtures with water and used the results to develop a computational model for solvent selection. The results showed a 50/50 mixture of methanol and water performed the best because it reduces reactor pressure and doesn't interfere with the microbes and lowers the break-even cost of the product by 24%.

Bacteria and other microbes can convert plant fibers into sustainable fuels and chemicals used to make plastics, medicines, and other products. But chemicals used in processing or in the plants themselves are an obstacle because they can kill the cells or slow fermentation. Researchers are looking for ways to modify natural efflux pumps to selectively remove these toxins, but testing the vast number of possible variations is impractical using traditional lab techniques. Data generated for this project are being used to train artificial intelligence models to predict which mutations are most likely to be effective.