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Monitoring of metal phytofiltration performance by micro-XRF methodology

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Abstract

In this work, micro-XRF was considered as a possible technique for monitoring the rate of incorporation of Cu and Zn into aquatic plants of a laboratory-scale phytofiltration system. This system employed Salvinia biloba Raddi under controlled conditions of light and nutrients. This aquatic plant is being considered as an efficient hyperaccumulator of Cu and Zn and is widely spread in South American lakes and rivers. One set of plants was exposed to 40 ppm w/w of Cu and another to 40 ppm w/w of Zn. The analytical procedure was based on the periodic in vivo quantitative analysis of Cu and Zn at selected points in the plants using micro-XRF. The accuracy of this quantification was effectively improved with the assistance of the Monte Carlo XMI-MSIM simulation code. In order to establish the input parameters of this software, careful measurements of the experimental parameters necessary for the correct modeling of the micro-XRF spectrometer were performed. After that, specially manufactured standards made of tissue equivalent material were employed to validate the configuration of the simulation code and input parameters. It was fulfilled by the comparison of measured and simulated micro-XRF spectra of these standards. Once the configuration code and input parameters were verified, two strategies were considered for the application of Monte Carlo simulation for elemental quantification in plants: an iterative process and inverse method established with external virtual standards. Benefits and drawbacks of both approaches to improve the monitoring of phytofiltration systems were carefully discussed.

Graphical abstract: Monitoring of metal phytofiltration performance by micro-XRF methodology

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Article information


Submitted
02 Mar 2021
Accepted
29 Apr 2021
First published
29 Apr 2021

Anal. Methods, 2021, Advance Article
Article type
Paper

Monitoring of metal phytofiltration performance by micro-XRF methodology

V. M. Sbarato, G. E. Falchini, H. J. Sánchez and R. D. Perez, Anal. Methods, 2021, Advance Article , DOI: 10.1039/D1AY00360G

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