Integrating the Gutzeit method with X-Ray fluorescence spectroscopy for rapid quantification of inorganic arsenic in selected beverages

A paper-based visual colorimetric platform for rapid detection of arsenic in the environment

Arsenic is a highly toxic heavy metal that poses significant environmental and health risks. Major sources of arsenic pollution include wastewater and waste discharges from industrial and mining activities, as well as arsenic-containing pesticides and herbicides used in agriculture. This study employed the arsenic spot method, utilizing test strips prepared with mercury bromide as a reactive sensor, to conduct semi-quantitative detection of inorganic arsenic in water, soil, and oil field chemicals used in oil extraction processes. Detection was performed through colorimetric analysis. Experimental results revealed the following detection limits for the test strip: 0.05 mg L-1 for water samples, 0.25 mg L-1 for soil samples, and 0.05 mg L-1 for water-soluble oil field chemicals. The detection results aligned with those obtained via inductively coupled plasma mass spectrometry, confirming the reliability of the method. Consequently, the arsenic spot colorimetry technique is a rapid and effective tool for the semi-quantitative determination of inorganic arsenic in various samples, significantly reducing analysis time.

Speciation analysis of arsenic in honey using HPLC-ICP-MS and health risk assessment of water-soluble arsenic

It is well known that arsenic is one of the most toxic elements. However, measuring total arsenic content is not enough, as it occurs in various forms that vary in toxicity. Since honey can be used as a bioindicator of environmental pollution, in the present study the concentration of arsenic and its species (As(III), As(V), DMA, MMA and AsB) was determined in honey samples from mostly Poland and Ukraine using HPLC-ICP-MS hyphenated technique. The accuracy of proposed methods of sample preparation and analysis was validated by analyzing certified reference materials. Arsenic concentration in honey samples ranged from 0.12 to 13 μg kg-1, with mean value of 2.3 μg kg-1. Inorganic arsenic forms, which are more toxic, dominated in honey samples, with Polish honey having the biggest mean percentage of inorganic arsenic species, and Ukrainian honey having the lowest. Furthermore, health risks resulting from the consumption of arsenic via honey were assessed. All Target Hazard Quotient (THQ) values, for total water-soluble arsenic and for each form, were below 1, and all Carcinogenic Risk (CR) values were below 10-4, which indicates no potential health risks associated with consumption of arsenic via honey at average or recommended levels.

Plasmonic nanoparticle's anti-aggregation application in sensor development for water and wastewater analysis

Colorimetric sensors have emerged as a powerful tool in the detection of water pollutants. Plasmonic nanoparticles use localized surface plasmon resonance (LSPR)-based colorimetric sensing. LSPR-based sensing can be accomplished through different strategies such as etching, growth, aggregation, and anti-aggregation. Based on these strategies, various sensors have been developed. This review focuses on the newly developed anti-aggregation-based strategy of plasmonic nanoparticles. Sensors based on this strategy have attracted increasing interest because of their exciting properties of high sensitivity, selectivity, and applicability. This review highlights LSPR-based anti-aggregation sensors, their classification, and role of plasmonic nanoparticles in these sensors for the detection of water pollutants. The anti-aggregation based sensing of major water pollutants such as heavy metal ions, anions, and small organic molecules has been summarized herein. This review also provides some personal insights into current challenges associated with anti-aggregation strategy of LSPR-based colorimetric sensors and proposes future research directions.

Robust, reliable and quantitative sensing of aqueous arsenic species by Surface-enhanced Raman Spectroscopy: The crucial role of surface silver ions for good analytical practice

Arsenic speciation analysis is important for pollution and health risk assessment. Surface-enhanced Raman Spectroscopy (SERS) is supposed to be a promising detection technology for arsenic species owing to the unique fingerprints. However, further application of SERS is hampered by its poor repeatability. Herein, the role of surface silver ions on colloidal Ag was revealed in SERS analysis of arsenic species. Arsenic species were adsorbed on Ag nanoparticles (Ag NPs) driven by surface silver ions and were simultaneously sensed by the SERS "hot spots" generated from the aggregation of Ag NPs. So, the inconsistent SERS activities of Ag NPs synthesized from different batches can be significantly improved by modifying external silver ions onto Ag NPs (AgNPs@Ag⁺), Specific binding affinity of surface silver ions to arsenic species generated higher sensitivity (detection limit, 4.0 × 10^-11 mol L^-1 for arsenite, 8.0 × 10^-11 mol L^-1 for arsenate), wider linear range, faster response, cleaner spectra background and better reproducibility. Batch-to-batch reproducibility was significantly improved with a variation below 3.1%. The method was also demonstrated with drinking and environmental water with adequate recovery and high interference resistance. Our findings displayed good analytical practice of the surface silver ions derived SERS method and its great potential in the rapid detection of hazardous materials.

Toward a greener approach to detect inorganic arsenic using the Gutzeit method and X-ray fluorescence spectroscopy

Inorganic arsenic is a carcinogen repeatedly found in water and foods threatening global human health. Prior work applied the Gutzeit method and X-ray fluorescence spectroscopy to quantify inorganic arsenic based on a harmful chemical, i.e., mercury bromide, to capture the arsine gas. In this project, we explored silver nitrate as an alternative to mercury bromide for the capture and detection of inorganic arsenic. To compare the performance of mercury bromide and silver nitrate, two standard curves were established in the range from 0 to 33.3 µg/L after optimization of reaction conditions such as the quantity of reagents and reaction time. Our result shows silver nitrate-based standard curve had a lower limit of detection and limit of quantification at 1.02 µg/L and 3.40 µg/L, respectively, as compared to the one built upon mercury bromide that has limit of detection of 4.86 µg/L and limit of quantification of 16.2 µg/L. The relative higher sensitivity when using silver nitrate was contributed by the less interfering elements for X-ray fluorescence analysis and thus lower background signals. A commercial apple juice was studied for matrix inference, and the results show 85%-99% recoveries and 7.4%-24.5% relative standard deviation. In conclusion, we demonstrated silver nitrate is a better choice in terms of safety restrictions and detection capability at lower inorganic arsenic concentrations.