Potentiometric Phosphate Ion Sensor Based on Electrochemical Modified Tungsten Electrode

Rapid and Convenient Potentiometric Method for Determining Fluorosulfate, a Byproduct of the Fumigant and Greenhouse Gas Sulfuryl Fluoride

A fluorosulfate ion (FSO3-) is a hydrolysis product of sulfuryl fluoride (SO2F2), which is widely used to fumigate buildings, soil, construction materials, and postharvest commodities, and is a potent greenhouse gas. It is a potential marker for biological exposure to SO2F2 and for monitoring the progress of reactions used to scrub SO2F2 from fumigation vent gases. Here, we report a simple and inexpensive potentiometric method for determining FSO3- using a commercial nitrate-selective electrode and discuss its application. The method is suitable for solutions between 0.0025 mM and 660 mM FSO3- at initial pH between 5 and 9. Halide interference depends on its molar ratio to FSO3- and follows the sequence, F- < Cl- < Br- ≪ I-. Halide interference can be eliminated by adding silver sulfate. Interference by bicarbonate can be eliminated by H2SO4 pretreatment, and interference by phosphate or pyrophosphate by MgSO4 addition. Sulfate does not interfere, as it does in ion chromatography. Satisfactory method detection limits for FSO3- in spiked aqueous extracts of 11 fruits were obtained. The method accurately quantified the yield of FSO3- relative to that of F- in base hydrolysis of SO2F2. This study demonstrates that the developed method is highly selective, convenient, and sensitive and thus can be of great value in practice.

Europium oxide modified reduced graphene oxide composite for trace detection of hydrogen phosphate ions in soil samples

The phosphate (PO43-) ion is a constituent of the environment, soil, plants, and animals. There should be a real-time and portable phosphate detection sensor. Herein we propose a colorimetry based sensitive method for hydrogen phosphate (HPO42-) ions detection using europium oxide modified reduced graphene oxide composite (Eu2O3-RGO) and gold nanoparticles (Au NPs). We detect the HPO42- by observing the anti-aggregation of gold nanoparticles. In the presence of a Eu2O3-RGO composite, the Au NPs underwent an aggregation process, causing a colour change of Au NPs from wine red to wine blue. Once Eu-modified RGO was pre-mixed with HPO42- ions and introduced into Au NPs, the Eu nanoparticles in the Eu-modified RGO were attracted to the HPO42- ions. Because of this, the aggregated Au NPs started to anti-aggregate, and the colour of Au NPs changed from wine blue to wine red. The calibration curve of the sensor goes from 0 nM to 500 nM concentration of HPO42- ions. Our sensor has a detection limit of 0.08 nM, which is lower than the reported values. This improved lower detection limit is probably due to the use of RGO, which according to the literature review, can adsorb phosphate ions onto its surface. We optimized the incubation time and europium oxide (Eu2O3) nanoparticle concentration to improve the sensor's sensitivity. Lastly, we tested an agricultural sample using our developed method.

Comparative analysis of the capability of the extended biotic ligand model and machine learning approaches to predict arsenate toxicity

Assessment of inorganic arsenate (As(V)) is critical for ensuring a sustainable environment because of its adverse effects on humans and ecosystems. This study is the first to attempt to predict As(V) toxicity to the bioluminescent bacterium Aliivibrio fischeri exposed to varying As(V) dosages and environmental factors (pH and phosphate concentration) using six machine learning (ML)-guided models. The predicted toxicity values were compared with those predicted using the extended biotic ligand model (BLM) we previously developed to evaluate the toxic effect of oxyanion (i.e., As(V)). The relationship between the variables (input features) and toxicity (output) was found to play an important role in the prediction accuracy of each ML-guided model. The results indicated that the extended BLM had the highest prediction accuracy, with a root mean square error (RMSE) of 12.997. However, with an RMSE of 14.361, the multilayer perceptron (MLP) model exhibited quasi-accurate prediction, despite having been trained with a relatively small dataset (n = 256). In view of simplicity, an MLP model is compatible with an extended BLM and does not require expert knowledge for the derivation of specific parameters, such as binding fraction and binding constant values. Furthermore, with the development and employment of reliable in-situ sensing techniques, monitoring data are expected to be augmented faster to provide sufficient training data for the improvement of prediction accuracy which may, thus, allow it to outperform the extended BLM after obtaining sufficient data.

Progress and current trends in the electrochemical determination of phosphate ions for environmental and biological monitoring applications

The determination of phosphate ions in biological testing is critical for environmental safety. A reliable and accurate method is required to measure the true phosphate ion concentrations; in this regard, the electrochemical method is preferable because of its simple operation, fast response, and high sensitivity. By compiling existing electroanalytical techniques, researchers can compare the advantages and disadvantages of each method. This review examines the progress and recent advances in electrochemical sensing strategies adapted for the determination of phosphate ions in the environmental and during biological monitoring. We first discuss the history of phosphorus and the development of methods to detect phosphates. The recognition elements of phosphate ion sensors for environmental applications include metal-based, nanomaterial-based, carbon-based, and enzymatic electrodes. Phosphate determination in biological samples, such as blood serum, drugs, and other biological fluids, such as urine and saliva, as well as phosphate esters, is also discussed. The final part of our review addresses the current challenges that phosphate sensing technology faces and illustrates future opportunities for more reliable phosphate detection.