Oral Presentation Society of Environmental Toxicology and Chemistry Australasia 2021

Combining Surface Enhanced Raman Spectroscopy and DFT for detection of pharmaceutical pollutants in the aquatic environment (#132)

Timothy TO Ong 1 , Jiri JK Kessler 2 , Ewan EB Blanch 1 , Oliver OJ Jones 1
  1. RMIT University, Melbourne, VIC, Australia
  2. Institute of Organic Chemistry and Biochemistry, The Czech Academy of Sciences, Prague, Czech Republic

Environmental pollution is usually monitored via mass spectrometry-based methods which are sensitive but have several disadvantages. The instruments themselves are expensive, require specialized training to use and cannot be taken into the field, while samples require extensive pre-treatment and processing prior to analysis. Analytical methods which match the sensitivity of mass spectrometry but could be deployed in-situ and require minimal sample processing would be desirable. We propose Surface Enhanced Raman Spectroscopy (SERS) as a method able to meet these requirements. SERS is a surface-sensitive technique that enhances Raman scattering by utilising rough metal surfaces or by nanostructures such gold or silver nanoparticles. SERS gives selective spectral enhancement; increases in sensitivity of 104 to 108 are commonly reported.


We demonstrate the development of SERS methodology for the detection of pharmaceuticals in aquatic systems. Citrate reduced gold nanoparticles were used as the SERS substrate, with detection in the ppm range for a variety of commonly prescribed pharmaceuticals. Our current results show that SERS can detect the pharmaceuticals down to 1ppm concentration in treated wastewater samples. We have also generated spectra with enough detail to discern between pharmaceuticals with similar molecular structures.


In collaboration with Charles University, Prague, we use a novel DFT model to predict the SERS response for pharmaceuticals dependent on its molecular orientation on the substrate surface. To our knowledge, modelling this level of complexity of interaction has not been reported previously. The aim of this collaborative project is to develop a model that can accurately predict SERS responses for a chosen analyte and to better interpret experimental SERS data. Our preliminary models show that for Paracetamol, the amide group of the Paracetamol molecule is in close proximity to the AuNP surface. The preliminary computational models have shown that we can predict SERS activity of pharmaceutical pollutants in environmental contexts.