Computational hints for the simultaneous spectroscopic detection of common contaminants in water

Abstract

In this work, we present a reliable computational methodology with the capability to aid in the identification of ionic water contaminants, such as nitrite, nitrate, and thiocyanate ions, based on the use of Resonance Raman (RR) spectroscopy. The method combines an exhaustive configurational sampling that fully captures the structural complexity inherent to aqueous solutions with state–of–the–art computational techniques that accurately simulate the response properties originating Resonance Raman signals of molecules in solution. Our computational findings show that it is possible to limit the quantum mechanical treatment to only a few explicit water molecules in order to capture the relevant interactions, and thus reproduce the available experimental spectra for NO2- and NO3-. Once validated, the methodology is applied to the prediction of the RR spectrum of aqueous SCN−. Our results indicate that by using an incident wavelength of 210 nm, the three emerging contaminants can be simultaneously detected in an aqueous matrix, thus avoiding the laborious indirect measurements used in current protocols. The designed protocol offers generalized explicit benefits: simultaneous detection of pollutants whose absorption spectra overlap, and because of the very nature of RR, pushes detection limits to lower concentrations.