21 Jun 2023

Comparison of the Quantitation of Heavy Metals in Soil Using Handheld LIBS, XRFS, and ICP-OES

Abstract: Handheld laser-induced breakdown spectroscopy (LIBS) is an emerging analytical technique that shows the potential to replace X-ray fluorescence spectroscopy (XRFS) in the field characterization of soils containing heavy metals. This study explored the accuracy and precision of handheld LIBS for analyzing soils containing copper and zinc to support LIBS as a re-placement for XRFS technology in situ. Success was defined by handheld LIBS results that could be replicated across field analyzers and verified by inductively coupled plasma–optical emission spectrometry (ICP-OES). A total of 108 soil samples from eight military installations were pressed into 13 mm pellets and then analyzed by XRFS and LIBS. Handheld LIBS has a spot-size area 100-fold smaller than that of XRFS, and though it provided accurate measurements for NIST-certified reference materials, it was not able to measure unknown soils of varying soil texture with high particle size variability, regardless of sample size. Thus, soil sample particle size heterogeneity hindered the ability to provide accurate results and replicate quantitation results across LIBS and XRFS. Increasing the number of particles encountered by each shot through particle size reduction improved both field-analyzer correlation and the correlation between handheld LIBS and ICP-OES from weak (<15%) to strong (>80%).

8 Nov 2022

A 𝘬-Means Analysis of the Voltage Response of a Soil-Based Microbial Fuel Cell to an Injected Military-Relevant Compound (Urea)

Abstract: Biotechnology offers new ways to use biological processes as environmental sensors. For example, in soil microbial fuel cells (MFCs), soil electro-genic microorganisms are recruited to electrodes embedded in soil and produce electricity (measured by voltage) through the breakdown of substrate. Because the voltage produced by the electrogenic microbes is a function of their environment, we hypothesize that the voltage may change in a characteristic manner given environmental disturbances, such as the contamination by exogenous material, in a way that can be modelled and serve as a diagnostic. In this study, we aimed to statistically analyze voltage from soil MFCs injected with urea as a proxy for gross contamination. Specifically, we used 𝘬-means clustering to discern between voltage output before and after the injection of urea. Our results showed that the 𝘬-means algorithm recognized 4–6 distinctive voltage regions, defining unique periods of the MFC voltage that clearly identify pre- and postinjection and other phases of the MFC lifecycle. This demonstrates that 𝘬-means can identify voltage patterns temporally, which could be further improve the sensing capabilities of MFCs by identifying specific regions of dissimilarity in voltage, indicating changes in the environment.