Project 1: Streamlined Identification of PAHs/PACs in Environmental Samples Using Ultracompact Spectroscopy Platforms and Machine Learning

The overall aim of this project is the development of new and innovative approaches to ultrasensitive detection and identification of polycyclic aromatic hydrocarbon (PAH) compounds and their derivatives (polycyclic aromatic compounds, or PACs). These compounds are present at superfund sites, introduced into the air, water and soil, resulting in human exposure, posing numerous hazards to human health. Our project is motivated by the hypothesis that PAH parent molecules can be chemically transformed under environmental and biological conditions into more active, toxic and mutagenic (PAC) species, and that fieldable, compact methods will greatly improve our ability to rapidly identify and monitor all variants within this entire family of compounds.

Our hypothesis is that substantial improvements in PAH/C detection and identification capabilities that would ultimately result in transformative new detection technologies that could greatly facilitate the evaluation of the environmental and biological impact of superfund sites on nearby resident populations.

Our technological advancements are based on our decades-long expertise in the accurate electromagnetic design (Nordlander), fabrication and characterization of nanoengineered metallic structures for surface-enhanced spectroscopies (Halas), combined with recent breakthroughs in machine learning (Patel), applied to the identification of chemical components of complex mixtures by Raman and IR spectroscopy for the first time.

Our project objectives are to achieve:

• Detection sensitivities similar to GC-MS-based methods using inexpensive yet high-sensitivity spectroscopic (Raman and Infrared) approaches
• Capabilities of identifying PAH/Cs in ultracompact geometries that could ultimately be developed into fieldable testing and monitoring tools designed to be compatible with passive patient monitoring methods.
• The ability to identify multiple PAH/Cs when present within samples, without the need for complex separation and purification steps, by applying machine learning approaches.

• Design, develop, and demonstrate high-sensitivity PAH and PAC detection based on surface-enhanced Raman spectroscopy (SERS), using specially designed and engineered metallic nanoparticles and nanostructures. We will examine the hypothesis that substrates functionalized specifically for the separation/partitioning of PAH/PACs onto detection platforms from water-borne, soil or sediment samples can facilitate the detection of these analytes at ppb and even sub-ppb levels.
   
• Design, develop and demonstrate ultrasensitive identification of “unknown” PAH compounds (PACs) through their spectroscopic signature, by combining SERS with surface-enhanced Infrared Absorption spectroscopy (SEIRA). Our working hypothesis is that PAH/C identification should be possible by combining both SERS and SEIRA detection approaches into a substrate with optimally enhanced signals.
   
• Design, develop and demonstrate ultracompact, all-in-one Raman detector/analyzers for PAH/PACs based on electronic, not optical, detection, for air, water and soil samples. These devices can serve as ultracompact, on-chip, dual-spectroscopy detector, entirely eliminating the need for the bulky and sensitive monochromators currently used for spectroscopic detection.

David Renard, Shu Tian, Arash Ahmadivand, Christopher J. DeSantis, Benjamin D. Clark, Peter Nordlander and Naomi J. Halas, Polydopamine-stabilized Aluminum Nanocrystals: Aqueous Stability and Benzo[a]pyrene Detection, ACS Nano 13, 3117-3124 (2019).

Bob Y. Zheng, Benjamin Cerjan, Hangqi Zhao, Ben Cerjan, Sadegh Yazdi, Emilie Ringe, Peter Nordlander, and Naomi J. Halas, “A room-temperature mid-infrared photodetector for on-chip molecular spectroscopy”, Applied Physics Letters 113, 110105 (2018).