Plasmonics for sustainable technologies and green energy

Alexandra Boltasseva of Purdue University discusses her team’s work with nanophotonics and plasmonics, from advancing materials to machine-learning-assisted designs
04 August 2023
Karen Thomas
Alexandra Boltasseva of Purdue University

“Being a professor provides a unique opportunity to combine many different jobs, and that is what I value the most,” says Alexandra Boltasseva, the Ron and Dotty Garvin Tonjes Professor of Electrical and Computer Engineering at Purdue University. Those various jobs include being a mentor, a teacher, a researcher, an editor, and science communicator, as well as serving and reaching out to both the scientific community and society at large. She also devotes time to workforce development in the emerging fields of quantum science and engineering, and participates in a multitude of professional societies’ initiatives run by SPIE, Optica, MRS, and IEEE.

At Purdue, Boltasseva works in the areas of nano- and quantum photonics, plasmonics, and optical metamaterials. The central focus of her research is finding new ways to realize photonic devices, from their material growth and advanced designs to nanofabrication and device demonstrations.

Boltasseva will be discussing advanced machine-learning-assisted photonic designs, materials optimization, and the development of environmentally friendly, large-scale fabrication techniques at 2023 SPIE Optics + Photonics in San Diego.

What led you to work with nanophotonics?
Nanophotonics is a perfect example of a highly interdisciplinary field where the convergence of optics, nano- and quantum science, computation physics, and materials engineering leads to both fundamental discoveries of new physical effects and the rise of innovative, disruptive technologies. It is intriguing that nanophotonics continues to open new chapters of physics and its applications. It is such a rich research field.

What are some of your research lab’s projects that you’re most excited about?
One of the directions we are very much excited about is how to apply classical machine-learning algorithms to achieve highly efficient photonic designs, and to enable faster, more accurate measurements and novel imaging/sensing schemes towards real-time quantum materials metrology.

AI methods can provide unconventional engineering solutions across multiple fields, and Purdue’s Elmore ECE Emerging Frontiers Centers initiative on “Crossroads of Quantum and AI” leverages ML to explore nano- and quantum photonics avenues. We also envision that tremendous advances will happen when the emerging quantum simulators are applied to solve the existing problems in optical design, higher sensitivity sensors, and nanometer-scale imaging systems.

Have you worked with machine-learning-assisted designs? If so, how has machine learning helped with photonic design?
ML algorithms enable high-efficiency, unorthodox, unconventional designs that go well beyond our engineering input, as well as enabling enormous speed-up in designing high-performance optical structures. Importantly, ML-assisted approaches can potentially provide globally optimized solutions, and even unlock new physics when combined with the emerging quantum simulators.

How can plasmonics be used in the development of green-energy sources?
My colleagues Jennifer A. Dionne and Vladimir M. Shalaev have recently put together a wonderful review article on “Nanophotonics for a sustainable future” that helps answer this question. Metal-based nanophotonics can, for example, boost photovoltaic, thermophotovoltaic, and photocatalysis efficiency, and enable novel single-molecule-level sensors as well as promising new exotic technologies for space and deep-sea exploration.

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