Ultrasensitive determination of selenium in food samples and its speciation in water and beverages using thiosemicarbazide-incorporated graphene and total-reflection X-ray fluorescence spectrometry

The paper presents a new analytical procedure for the determination and speciation of trace and ultratrace selenium in water, beverages, seafood, milk, and vegetables. The developed method is based on the dispersive micro-solid phase extraction with the use of new thiosemicarbazide-incorporated graphene as a solid sorbent, in combination of the total-reflection X-ray fluorescence spectrometry (TXRF). As a result, we have created an auspicious analytical tool for fast and sensitive analysis of samples with a complex matrix. Regardless of the specimen type, the method is characterized by a very low detection limit of 1.7 pg mL-1 and high precision. The developed strategy allowed us to solve common problems associated with selenium loss during the sample preparation for the TXRF measurement and also improve its performance toward the analysis of beverages and high saline/solid samples, which may even be impossible to perform using standard sample preparation procedures for a TXRF measurement.

A novel method for multi-pollutant monitoring in water supply systems using chemical machine vision

Drinking water is vital for human health and life, but detecting multiple contaminants in it is challenging. Traditional testing methods are both time-consuming and labor-intensive, lacking the ability to capture abrupt changes in water quality over brief intervals. This paper proposes a direct analysis and rapid detection method of three indicators of arsenic, cadmium, and selenium in complex drinking water systems by combining a novel long-path spectral imager with machine learning models. Our technique can obtain multiple parameters in about 1 s. The experiment involved setting up samples from various drinking water backgrounds and mixed groups, totaling 9360 injections. A raw visible light source ranging from 380 to 780 nm was utilized, uniformly dispersing light into the sample cell through a filter. The residual beam was captured by a high-definition camera, forming a distinctive spectrum. Three deep learning models-ResNet-50, SqueezeNet V1.1, and GoogLeNet Inception V1-were employed. Datasets were divided into training, validation, and test sets in a 6:2:2 ratio, and prediction performance across different datasets was assessed using the coefficient of determination and root mean square error. The experimental results show that a well-trained machine learning model can extract a lot of feature image information and quickly predict multi-dimensional drinking water indicators with almost no preprocessing. The model's prediction performance is stable under different background drinking water systems. The method is accurate, efficient, and real-time and can be widely used in actual water supply systems. This study can improve the efficiency of water quality monitoring and treatment in water supply systems, and the method's potential for environmental monitoring, food safety, industrial testing, and other fields can be further explored in the future.

Determination and speciation of arsenic in drinking water samples by X-ray spectrometry technique

Arsenic is ranked as the first compound in the Substance Priority List 2023 by the Agency for Toxic Substances and Disease Registry (ATSDR). The most prominent entrance to the human body is through drinking water wherein the predominant species are arsenite and arsenate. The more toxic As(III) has rigorously threatened human health worldwide; hence, speciation and separation are the need of the hour. In this article, we have reported a simple method of arsenic speciation by wavelength dispersive X-ray fluorescence (WD-XRF) spectrometer. Valence to core (VtC) electronic transitions, i.e., AsKβ2,5 fluorescence lines were used for arsenic speciation. This speciation study by WD-XRF entails direct measurement of activated alumina pellets containing arsenate and arsenite species adsorbed from water sample without separation of the trivalent and pentavalent species. This is the first report wherein the X-ray technique has been explored for speciation analysis of arsenic and the biggest advantage of the method lies in its applicability to direct analysis of synthesized nanotubes or other solid-phase extraction sorbents entrapping both the arsenic species. For determination of total arsenic using activated alumina as adsorbent, the most intense AsKα1,2 analytical lines were used and the instrumental limit of detection and the lower limit of quantification were 0.23 μg/L and 0.89 μg/L, respectively. For speciation, these limits were calculated to be 50 μg/L and 200 μg/L, respectively.

Gold nanoparticles embedded Fe-BTC (AuNPs@Fe-BTC) metal-organic framework as a fluorescence sensor for the selective detection of As(III) in contaminated waters

In this article, a new off-mode fluorescent platform based on the metal-organic framework (MOF) is introduced as a highly selective and rapid chemical sensor for the detection of As(III) in water and wastewater samples. A typical Fe-BTC (BTC = 1,3,5-benzenetricarboxylate or trimesic acid) MOF was used as a porous template for loading gold nanoparticles (AuNPs@Fe-BTC MOF). The physicochemical properties of AuNPs@Fe-BTC MOF were characterized by Fourier-transform infrared spectroscopy (FT-IR), Field emission scanning electron microscopy (FESEM), Energy-dispersive X-ray spectroscopy (EAX), element mapping (MAP) and X-ray diffraction (XRD) analysis. This sensing method for As(III) ions is based on the fact that the fluorescence intensity of AuNPs@Fe-BTC MOF sensor decreases in proportion to the increase in As(III) concentration. The main effective factors on the performance of the sensor signal such as MOF dosage, sonication time, pH and reaction time were optimized. Under optimized conditions, the calibration graph was linear in the concentration range of 0.5-380 ng mL-1 of As(III) and the limit of detection was 0.2 ng mL-1. The proposed method was successfully validated by addition/recovery experiments by the determination of As(III) in four river water and two wastewater effluent samples.

Fast and Sensitive Determination of Cadmium and Selenium in Rice by Direct Sampling Electrothermal Vaporization Inductively Coupled Plasma Mass Spectrometry

In this work, a direct solid sampling device based on modified graphite furnace electrothermal vaporization (GF-ETV) with inductively coupled plasma mass spectrometry (ICP-MS) was established for the simultaneous detection of trace selenium and cadmium in rice samples. A bypass gas was first designed in GF-ETV to improve the device’s analytical sensitivity and precision. The ashing and vaporization conditions, the flow rates of the Ar carrier and the bypass gases of ICP-MS were all investigated. Under the optimized conditions, the limits of detection (LODs) for Se and Cd were 0.5 μg kg−1 and 0.16 μg kg−1, respectively; the relative standard deviations (RSDs) of repeated measurements were within 8% (n = 6). The recoveries of Cd and Se in rice samples were in the range of 89−112% compared with the microwave digestion ICP-MS method, indicating good accuracy and precision for the simultaneous detection of Se and Cd in rice matrix. The whole analysis time is <3 min without the sample digestion process, fulfilling the fast detection of Se and Cd in rice samples to protect food safety.

Determination and speciation of trace inorganic arsenic species in water samples by using metal organic framework mixed-matrix membrane and EDXRF spectrometry

A facile method to selectively determine trace As(V) species in the existence of As(III) one in water samples was developed, which was based on the batch adsorption process by using a miniaturized MIL-101(Fe) mixed-matrix membrane (MOF-MMM) followed by a direct determination through energy dispersive X-ray fluorescence (EDXRF) spectrometry. The quantitative adsorption of As(V) was achieved at pH (3-6) from 30 mL sample in 120 min of equilibrium time by employing the membrane with a monolayer adsorption capacity of Q_o = 1.953 mg g^-1. The direct determination of As(V) adsorbed on the membrane by EDXRF spectroscopy provided a method, not only easy-to-use and operable without elution stage, but also cost effective due to low gas consumption during the analysis. With a limit of detection of 0.094 μg L^-1, analytical performance of the method, which was evaluated on fortified real water samples with three levels of As(V) (5, 10 and 50 μg L^-1), demonstrated good recoveries in the range of 98(±3)-105(±10)%. Furthermore, the speciation of As(III) and As(V) in the fortified real samples containing other ionic species was also successfully achieved by described approach with characteristics of simple, cheap, viable and reproducible.

Real-Time On-Site Multielement Analysis of Environmental Waters with a Portable X-ray Fluorescence (pXRF) System

Strict regulations are in place to control the effluents of mining sites and other industries. Heavy metal contamination of aquatic systems caused by leakages is difficult to mitigate as it takes time to detect and localize the leak. Dynamic sampling would drastically reduce the time to locate leakages and allow faster actions to reduce the impact on the environment. The present study introduces a novel portable multielement water analysis system to simultaneously measure Mn, Ni, Cu, Zn, Pb, and U in water samples from natural sources within 15 min from the sampling. The metals are preconcentrated from a 10 mL water sample into a nanoporous filter based on bisphosphonate-modified thermally carbonized porous silicon. The metals can be conveniently analyzed from the filter with a portable XRF analyzer in field conditions. The system was empirically calibrated for a lake water matrix with neutral pH and low alkaline metal concentration. A strong correlation between the XRF intensities and the ICP-MS results was obtained in a concentration range from 50 to 10 000 μg/L. With a DPO-2000C XRF analyzer, the detection limits were 103, 86, 92, 35, 44, and 43 μg/L for Mn, Ni, Cu, Zn, Pb, and U, respectively. The corresponding values with X-MET8000 Expert Geo were 137, 46, 62, 38, 29, and 54. The system was successfully validated with simulated multielement lake water samples and piloted in field conditions. The system provides an efficient way to monitor metals in environmental waters in cases where quick on-site results are needed.

Phosphomethylated Polyethyleneimine-immobilized Chelating Resin: Role of Phosphomethylation Rate on Solid-Phase Extraction of Trace Elements

Chelating resins immobilizing phosphomethylated polyethyleneimine (PM-PEI) with different phosphomethylation (PM) rates were prepared by using different amounts of both phosphonic acid and paraformaldehyde in the phosphomethylation of PEI immobilized on a methacrylate resin as a base resin. The extraction of many elements improved with increasing PM rate; REEs, Be, Fe, Mo, Ti, and V were quantitatively extracted at pH 2. The elution of the elements tended to become difficult with increasing PM rate. When a PM-PEI resin with a PM rate of 0.26 was used, REEs and Be could be eluted using 0.2 mol L^-1 EDTA solution adjusted to a pH of 7 and 3 mol L^-1 nitric acid, respectively, although the elution of Fe, Mo, Ti, and V was insufficient. The PM-PEI resin could be reused at least 10 times to recover REEs and Be without the influence of any other elements. The PM-PEI resin could be applied to a recovery test using artificial seawater spiked with REEs, except for Sc, Tm, Yb, and Lu, and the separation of the REEs in NIST SRM 1515 Apple Leaves.