There are many forms of energy around us: light, heat, vibrations, wind, electromagnetic fields, fluid flow, waves, organic waste, etc. At large scale, many of these energy sources already play a significant role in powering our society and are projected to become dominant contributors by 2040. On the smaller scale, exciting scientific and engineering challenges must be overcome to harness these energy sources.
As we begin the New Year we’ll take a moment to celebrate some recent accomplishments and look towards the future: winners of the 2017 Rustum and Della Roy Awards will be announced, new faculty will be highlighted, and I’ll provide an update on some MRI strategic initiatives.
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By Ty Tkacik
Despite the prevalence of synthetic materials across different industries and scientific fields, most are developed to serve a limited set of functions. To address this inflexibility, researchers at Penn State, led by Hongtao Sun, assistant professor of industrial and manufacturing engineering (IME), have developed a fabrication method that can print multifunctional “smart synthetic skin” — configurable materials that can be used to encrypt or decrypt information, enable adaptive camouflage, power soft robotics and more.
By Sam Sholtis
Oil-in-water droplets respond to chemical cues by forming arm-like extensions that resemble filopodia, which are used by living cells to sense and explore their environment. A research team led by chemists at Penn State studies the droplets to glimpse how matter may have transitioned to life billions of years ago. The researchers dissected the mechanism through which these arms form and showed that they respond directionally, growing toward or away from specific chemicals.
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305C Osmond Lab
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104 Davey Lab
Artificial intelligence (AI) is increasingly used to analyze medical images, materials data and scientific measurements, but many systems struggle when real-world data do not match ideal conditions. Measurements collected from different instruments, experiments or simulations often vary widely in resolution, noise and reliability. Traditional machine-learning models typically assume those differences are negligible — an assumption that can limit accuracy and trustworthiness.