Rapid assessment of drug abuse from municipal wastewater is a new strategy that can overcome many limitations of traditional surveillance technologies by providing a more rapid and objective measure of drug use, which can alert communities about raising problems in their earlier stage. However, existing detection techniques are almost exclusively based on complex laboratory chemistry analytical equipment such as high-performance liquid chromatography in tandem with mass spectrometry (HPLC-MS), which demand tedious sample preparation with expensive reagents and substantive operator expertise. These methods are ill-suited to large scale, frequent monitoring of illicit drugs in municipal wastewater. In this NIH STTR Phase I proposal, E-Lambda LLC together with Oregon State University proposes to develop a rapid, portable, quantitative, and cost-effective assessment technique using smart surface-enhanced Raman scattering (S- SERS). The proposed S-SERS is a disruptive technique enabled by two game-changing innovations in the PIs research group, targeting practical technical hurdles of portable SERS sensing technique for rapid assessment of drug abuse from municipal wastewater. First, the diatomaceous SERS substrates, which are developed through previously funded research projects, will provide ultra-sensitive, uniform SERS enhancement factors to detect trace level of illicit drugs and the metabolic products using commercial portable Raman spectrometers. Second, advanced machine-learning methods based on quaternion principal components analysis and support vector regression algorithms, will resolve the grand challenges faced by traditional SERS analysis in quantitative sensing. The proposed S-SERS technique is expected to enable quantitative, multi-drug detection including opioid analgesics such as fentanyl, morphine, codeine, heroin, amphetamine, and other synthetic derivatives from municipal wastewater. The new wastewater testing technology proposed herein could provide a widespread and objective picture of drug use that would be consistent, scalable, cost-effective, and complement other epidemiological studies. The team will also work with multiple stakeholders including Benton County Health Department, City of Corvallis, and Oregon State University Accelerator to demonstrate a clear value proposition of the S-SERS technology for rapid assessment of drug abuse from municipal wastewater.
Public Health Relevance Statement: PROJECT NARRATIVE (RELEVANCE) This project aims to create a smart surface-enhanced Raman scattering (S-SERS) technique, which will enable rapid, portable, quantitative, and cost-effective assessment of illicit drugs from municipal wastewater. Particularly, the proposed technology combines diatomaceous SERS substrates with machine-learning analysis to overcome challenges faced by traditional SERS sensing. The success of this project will enable a widespread and objective picture of drug use that would be consistent, scalable, cost-effective, and complement other epidemiological studies.
Project Terms: Address; Amphetamines; Analytical Chemistry; base; Chemical Interference; Chemicals; Cities; Codeine; Collaborations; commercialization; Communities; Complement; Complex; cost effective; County; Data; Databases; density; design; Detection; Devices; Diatomaceous Earth; Drug abuse; drug testing; Drug usage; Effectiveness; Engineering; enhancing factor; Epidemic; epidemiology study; Equipment; Fentanyl; Funding; Goals; Growth; Health; Heroin; High Pressure Liquid Chromatography; Hot Spot; Illicit Drugs; improved; In Situ; innovation; Laboratories; Location; Machine Learning; machine learning algorithm; machine learning method; Mass Spectrum Analysis; Measures; Metabolic; Methods; Microfluidic Microchips; Monitor; Morphine; Municipalities; nanoparticle; Nature; Opioid Analgesics; opioid misuse; Oregon; particle; Pharmaceutical Preparations; Phase; plasmonics; portability; Preparation; Prevalence; Prevention program; Principal Component Analysis; Process; Production; programs; prototype; rapid technique; Reagent; regression algorithm; Research; Research Project Grants; Sampling; Small Business Technology Transfer Research; success; support vector machine; Surface; System; Techniques; Technology; Technology Assessment; Testing; Thin Layer Chromatography; Training; treatment program; trend; United States National Institutes of Health; Universities; Variant; vector; water sampling; Work
