A number of vulnerabilities, known collectively as deep learning adversaries, hold artificial intelligence (AI) back from its full potential in applications like improving medical imaging quality and computer-aided diagnosis.

Accurate predictive simulations of the electrochemical reactions that power solar fuel generators, fuel cells, and batteries could advance these technologies through improved material design, and by preventing detrimental electrochemical processes, such as corrosion. However, electrochemical reactions are so complex that current computational tools can only model a fraction of all relevant factors at one time — with limited accuracy. This leaves researchers reliant on the trial and error of significant and expensive experimentation.

In the wake of the COVID-19 pandemic, restaurants throughout New York City and elsewhere use bespoke outdoor structures to offer safer dining experiences for their customers. However, many of these installations do not adequately protect servers, physically separate diners, provide thermal comfort, or easily disassemble if street maintenance is needed. 

Fouling is a natural phenomenon that describes the tendency of proteins in water to adhere to nearby surfaces. It’s what causes unwanted deposits of protein to form during some food production or on biomedical implants, causing them to fail. Researchers at Rensselaer Polytechnic Institute are harnessing this process, which is typically considered a persistent challenge, to develop a versatile and accessible approach for modifying solid surfaces.

With communities across the nation experiencing a wave of COVID-19 infections, clinicians need effective tools that will enable them to aggressively and accurately treat each patient based on their specific disease presentation, health history, and medical risks.

A small energy harvesting device that can transform subtle mechanical vibrations into electrical energy could be used to power wireless sensors and actuators for use in anything from temperature and occupancy monitoring in smart environments, to biosensing within the human body.

TROY, N.Y. — The harsh conditions that equipment, satellites, and spacecraft are subject to in space pose significant challenges. Electronic systems must be protected from extreme heat and cold, while storage containers holding liquid propellants must be shielded from solar radiation.

A COVID-19 transmission model inspired by gas-phase chemistry is helping the Centers for Disease Control and Prevention (CDC) forecast COVID-19 deaths across the country.

Developed by Yunfeng Shi, an associate professor of materials science and engineering at Rensselaer Polytechnic Institute, and Jeff Ban, a professor of civil engineering at the University of Washington, the model uses fatality data collected by Johns Hopkins University and mobility data collected by Google to predict disease spread based on how much a population is moving within its community.