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.
Jainendra K. Jain, Evan Pugh University Professor and Erwin W. Müller Professor of Physics and holder of the Eberly Family Chair in the Penn State Eberly College of Science, has been awarded, along with two others, the 2025 Wolf Prize in Physics for “groundbreaking contributions to quantum matter and its topological potential” that revolutionized “our understanding of two-dimensional electron systems in strong magnetic fields.”
By Jamie Oberdick and Ashley WennersHerron
Newly achieved precise control over light emitted from incredibly tiny sources, a few nanometers in size, embedded in two-dimensional (2D) materials could lead to remarkably high-resolution monitors and advances in ultra-fast quantum computing, according to an international team led by researchers at Penn State and Université Paris-Saclay.
By Sarah Small
A biomaterial that can mimic certain behaviors within biological tissues could advance regenerative medicine, disease modeling, soft robotics and more, according to researchers at Penn State.
By Jamie Oberdick
Scientists at Penn State have harnessed a unique property called incipient ferroelectricity to create a new type of computer memory that could revolutionize how our devices work, such as using much less energy and being able to work in extreme environments like outer space.
The researchers noted that the societal benefits of this research could be significant. Traditional AI systems, especially those handling image recognition, consume significant energy. The ferroelectric transistors’ low power requirements present a sustainable alternative.
