Paper by ChE’s Nianqiang Wu and His Research Team Graces Cover of The Journal of Physical Chemistry C

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A paper published by the laboratory of Nianqiang Wu, the Armstrong/Siadat Endowed Professor of Materials Science & Engineering in the UMass Amherst Chemical Engineering (ChE) Department, was chosen as the cover article in the June 18th issue of The Journal of Physical Chemistry C. As the paper concludes, “This machine-learning-assisted numerical study provides guidelines for designing a large-area plasmonic metamaterial surface for different applications.” 

According to an article about the promising new field of plasmonics on the All the Science website, “Plasmons are density waves of electrons, created when light hits the surface of a metal under precise circumstances…They can theoretically encode a lot of information, more than what's possible for conventional electronics. Plasmonics is thought to embody the strongest points of both optical- and electronic-data transfer, allowing the fast transmission of information over very small wires.” 

In addition, plasmons have a strong ability to harvest light and to convert light energy to electric energy or chemical energy by coupling them with semiconductors.

The title of Wu’s Journal of Physical Chemistry C cover paper is “Machine-Learning-Assisted Light Management and Electromagnetic Field Modulation of Large-Area Plasmonic Coaxial Cylindrical Pillar/Ring Nanoarray Patterns.” It was published by Wu and several members of his lab: lead author Dr. Anyang Wang (postdoctoral fellow), Yingjie Hang (graduate student), Jiacheng Wang (graduate student), and Weirui Tan (research associate).

Many devices in our daily life and industrial sectors are built on the ability to manipulate light and harvest light’s energy, such as optical sensors, photodetectors, light-emitting devices (LEDs), solar cells, and photoelectrochemical cells. 

To maximize such light-management ability, Wu and his team have designed a nanoscale-array pattern, which has a similar structure to chips used in computer CPUs and USB jump drivers. Millions of gold nano-pillar/rings, with all dimensions less than 100 nanometers, are laid out on a solid chip in a long-range, periodic-patterned architecture.

Such a pattern generates plasmons, which harvest and control light. The light harvested can be manipulated to fulfill many functions, such as creating a strong electromagnetic field, emitting hot electrons, and converting light energy to electric energy or chemical energy. These functions are utilized to operate the above-mentioned devices. 

As described by Wang, “To maximize the performance of plasmonic nano-array patterns, it is essential to tune key light responses, optical properties, and energy losses by tuning the geometry of nano-sized pillars/rings and their layout on the chip.” 

The Journal of Physical Chemistry C. cover article provides a theoretical framework for tailoring the geometrical parameters of such coaxial cylindrical pillar/ring nano-array patterns toward desirable optical properties. 

Furthermore, artificial intelligence (AI) has been used to establish the relationship of the light-management manner with multiple geometric features of such nano-array patterns. Machine learning is employed to identify which features are most influential in determining the optical responses and local electric-field enhancement; for example, which are critical to amplifying signals of optical biosensors based on plasmonic nano-array biochips. 

The knowledge obtained in this study will promptly help identify an optimal configuration for the intended application, which will guide and speed up the fabrication process and save the fabrication costs.

As the website of the Wu Research Group explains, “Our research aims to gain fundamental understanding of charge transfer and energy transfer in electrochemical and optoelectronic​ materials and devices. It gives us a unique advantage in developing high-performance materials and devices with the ‘material-by-design’ strategy.” 

The group’s four main areas of research are tied together with fundamental discovery of charge-transfer and energy-transfer processes and build on the team’s interdisciplinary expertise in electrochemistry and plasmonics. (October 2024)