Interdisciplinary Research Integrating Materials Science and Engineering
Based on deep foundations, PRISM research is focused on emerging areas at the intersections between traditional fields such as the hard materials/soft materials interface. The work at PRISM is led by the faculty from nearly all of the academic departments in both the natural and life sciences and engineering. Research direction at PRISM falls broadly into five emerging themes:

Quantum Materials and Structures
Creating near-atomic scale structures exhibiting quantum processes that can lead to fundamental new properties.

photo of quantum computing
Ring of 100 Josephson junctions: an artificial 'atom" for quantum computing.
Photo credit: Andrei Vrajitoarea, Houck Group, ELE

Scalable Structures
Developing materials with complex properties and functions from the nanoscale to the large-area macroscale for a diverse set of applications.

photo of perovskites
Princeton researchers have refined the manufacturing of light sources made with crystalline substances known as perovskites, a more efficient and potentially lower-cost alternative to materials used in LEDs found on store shelves.
Photo credit: Sameer Khan, Fotobuddy

Photonics and Light-Matter Interactions
Enabling new limits in imaging, sensing and transmission capabilities through creation of new optical sources, optical processes, and nanostructured materials.

photo of one dimensional Si photonic crystals for coupling
One dimensional Si photonic crystals for coupling to erbium-based quantum emitters.
Photo credit: Alan Dibos, Thompson Group, ELE

Bio-Nano Intersection
Using small and highly parallel structures to learn about biology on the level of a single cell through new clinical tools and probes.

photo of bio-nano image

Theory and Computational
A focus on understanding the relationship between the macroscopic behavior of complex materials and their microstructures. This includes our current work on disordered and ordered particle packings, optimal multifunctional material design, and inverse optimization methods applied to self-assembly.