Researchers found a magnetic property in a class of materials that enables light manipulation on the nanoscale, with implications for applications such as information storage and energy harvesting.
Controlling light at the nanometer scale is essential for high-resolution imaging, biological sensors, and cell phones. However, having precise, efficient control over light remains a major challenge for the field of materials science and nanotechnology. Because light is an electromagnetic wave that carries no charge, it is difficult to manipulate with voltage or an external magnetic field, and using indirect ways to manipulate light at these scales is challenging because of how materials behave at atomically-thin states.
The different colors in this sample of iron phosphorous trisulfide (FePS3) correspond to regions with varying thicknesses, which form different “cavity” modes at different wavelengths. (Image: Penn Engineering Today)
By connecting the magnetic and optical properties of antiferromagnetic nanomaterials, this research opens doors for engineering light for new applications such as information storage and energy harvesting. “This class of materials is much broader with many more combinations to explore, including finding ways to raise the magnetic transition temperatures,” adds Jariwala. “We are now looking to find and design ways to manipulate light inside these materials using multiple control knobs and see how strongly we can tune them in real devices.”
Understanding order to disorder at the atomic scale opens possibilities for next-generation electronic devices
A Penn team has developed insight into the chemical and geometric mechanisms underlying the synthesis of new 2D materials, paving the way for next-gen devices, biomedical applications, and cleaner, quicker energy conversion and storage.
Penn buildings achieve LEED certifications, showcasing commitment to sustainability
Three recent building projects at the University of Pennsylvania have earned LEED Platinum, Gold, and Silver certifications, underscoring Penn’s ongoing commitment to sustainable design and construction.
OpenAI, DeepSeek, and Google vary widely in identifying hate speech
Neil Fasching and Yphtach Lelkes of the Annenberg School for Communication have found dramatic differences in how large language models classify hate speech, with especially large variations for language about certain demographic groups, raising concerns about bias and disproportionate harm.
