Researchers at Lehigh University are developing a faster, more accurate way to predict how metals solidify during 3D printing and other additive manufacturing processes. Supported by a three-year, $350,000 grant from the National Science Foundation, assistant professor Parisa Khodabakhshi is creating a physics-based, data-driven model that connects manufacturing process parameters with the resulting material microstructure. The approach aims to replace costly trial-and-error methods with efficient simulation tools that can guide the design of high-performance metal components. The project’s outcomes could accelerate innovation across industries that rely on advanced manufacturing—such as aerospace, automotive, and healthcare—while helping train the next generation of engineers and scientists.

Auburn University scientists have designed a new family of materials where the interaction between electrons residing periphery of molecules unlocks properties nature never intended. By anchoring special molecules onto stable surfaces like diamond, the team created electride arrangements that can be tuned to act as building blocks for quantum computers or as powerful catalysts for advanced chemistry. This discovery paves the way for technologies that promise faster, more efficient computing and entirely new ways to manufacture materials and medicines.

MIT physicists improved the stability of optical atomic clocks by reducing “quantum noise” — a fundamental measurement limitation. The work could enable more precise, portable optical atomic clocks that track even tinier intervals of time, up to 100 trillion times per second.

A National Science Foundation grant will support Anne Brown’s goal to enhance the technical and practical data science skills of students studying molecular bioscience.

Researchers have improved the ability of wearable health devices to accurately detect when a patient is coughing, making it easier to monitor chronic health conditions and predict health risks such as asthma attacks. The advance is significant because cough-detection technologies have historically struggled to distinguish the sound of coughing from the sound of speech and nonverbal human noises.