It is necessary to detect cadmium ions in seawater with high sensitivity because the pollution of cadmium ions seriously endangers the health and life of human beings. Nano-Fe3O4/MoS2/Nafion modified glassy carbon electrode was prepared by a drop coating method. The electrocatalytic properties of Nano-Fe3O4/MoS2/Nafion were measured by Cyclic Voltammetry (CV). Differential Pulse Voltammetry (DPV) was used to study the stripping Voltammetry response of the modified electrode to Cd2+. The optimal conditions were determined: In 0.1 mol/L HAc-NaAc solution, the solution pH was 4.2, the deposition potential was − 1.0 V, and the deposition time was 720 s, the membrane thickness was 8 μL. Under the optimum condition, the linear relation of Cd2+ concentration was found in the range of 5–300 μg/L, and the detection limit was 0.053 μg/L. The recovery of Cd2+ in seawater ranged from 99.2 to 102.9%. A composite material with simple operation, rapid response and high sensitivity was constructed for the determination of Cd2+ in seawater.Graphical abstract

The demand for multi-point water quality monitoring is increasing to solve the global problem of safe drinking water supply and environmental water contamination by industries. Therefore, compact devices are needed for on-site water quality analysis. On-site devices require low cost and high durability because they are placed outdoors, exposing them to strong ultraviolet rays and a wide range of temperatures. Our previous study reported on a compact and low-cost water quality meter that uses microfluidic devices with resin to monitor chemicals. In this study, we extended the fabrication range of the glass molding method to fabricate a glass microfluidic device with a 300 µm deep channel on a 50 mm in diameter substrate for constructing a low-cost and high-durability device. Finally, we developed a low-cost, highly robust glass device with a diamond-like carbon-coated channel surface to measure residual chlorine. The experimental results indicated that this device can endure outdoor conditions and be attached to small internet of things devices for analyzing chemical substances, such as residual chlorine.Graphical

A facile, quick, and sensitive ratiometric luminescence sensor is designed for detection aluminum ions in water samples using luminescence or eye-vision. This approach relies on the emission change of the europium(III) complex with 3-(2-naphthoyl)-1,1,1,-trifluoro acetone (3-NTA) after interaction with various concentration of aluminum ions. The addition of aluminum ions suppressed the Eu(III) emission at 615 nm under 333 nm excitation, while simultaneously enhancing the ligand emission at 480 nm. Optimum detection was obtained in methanol. The quantification of aluminum ions using ratiometric method was determined by plotting the luminescence ratio (F480nm/F615nm) versus aluminum ions concentration. The calibration plot was obtained within the range 0.1–100 µM with LOD = 0.27 µM. Additionally, the concentration of aluminum ions can be estimated semi-quantitatively by visually observing the luminescence colour change of the probe from red to light green and then to dark green after being excited by a UV lamp with 365 nm. As far as we are aware, this is the first luminescent lanthanide complex-based ratiometric probe for the detection of aluminum ions. The probe showed remarkable aluminum ions selectivity relative to that of other metal ions. The suggested sensor was used effectively to identify aluminum ions in water samples with good results.Graphical

In recent years, the development of nanomaterials-based peroxidase mimics as enzyme sensors has been attracting considerable interest due to their outstanding features, including potent stability, and cost-effectiveness toward natural enzymes. In this work, mesoporous silica nanoparticles functionalized by copper (Cu-MSN) were prepared as a new artificial enzyme for the first time through the sol-gel procedure. A comprehensive investigation of the catalytic activity of Cu-MSN was done through the oxidation of chromogenic peroxidase substrates, 3,3',5,5'-tetramethylbenzidine (TMB), and (2,2'-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt (ABTS), in the presence of H2O2. The results indicate that the peroxidase-like activity of the as-prepared sample is significantly higher than other nanoparticles. Additionally, for the study, a facile and rapid sensing method based on the enzyme-like activity of Cu-MSN to detect H2O2 and glutathione (GSH) was developed to examine the potency of the proposed biosensor. Preliminary analysis revealed that the limit of detection (LOD) of H2O2 and GSH is 0.2 and 0.0126μM, in the range of 0.9-100 and 0.042-1μM, respectively. These findings support the claims for the efficiency of the sensor in detection fields. Also, human serum was utilized as the real sample to obtain additional evidence.

Iodine in natural salt waters such as iodine-rich underground brine generally exists as iodide ions, and to extract iodide ions, it is necessary to oxidize them to nonpolar iodine molecules. In this study, we report that iodide ions in natural brine containing high concentrations of chloride ions can be quantitatively extracted without oxidation by extraction using ionic liquids. Extraction with the ionic liquids trihexyltetradecylphosphonium chloride and trioctyl ammonium chloride was found to be able to enrich trace amounts of iodide in brine samples up to 40- and 100-fold, respectively. The selectivity coefficients for I- over Cl- determined in this study clearly show that the ionic liquids have much higher I-/Cl- selectivity than typical strongly basic anion exchangers. Extraction with trihexyltetradecylphosphonium chloride was applicable as a preconcentration method for iodine determination by XRF analysis. Iodide ions extracted with trioctyl ammonium chloride were quantitatively stripped with an aqueous alkaline solution. These results indicate that ionic liquid extraction is useful for the separation and concentration of iodide from brines.

Mercury (Hg) is harmful to the human body. Its physical properties and toxicity differ greatly depending on its chemical form. The quantification of soluble Hg in soils or sediments is crucial for preventing further environmental contamination by Hg sources such as products, processes, and storage. In this study, the risk of leachable Hg that release from soil to the hydrosphere was evaluated by the sequential extraction procedure (SEP), a speciation method of a targeted element based on the solubility of its various compounds. The SEP, which consists of a four-channel circulating flow system (FCFS), was developed to reduce the time and amount of liquid required for SEP of leachable Hg in the Bloom method, which is a conventional SEP for Hg in a solid sample. The SEP with FCFS was optimized by considering the impact of extractant volume, circulation extraction time, and flushing of the extraction line. In the three-step (Fraction 1-3) sequential extraction of leachable Hg in soil samples, the optimized SEP with FCFS required 45mL of extractant and 2.5h, while the common batch SEP required 75mL of extractant and 49h. Furthermore, the combination of the SEP with FCFS for the leachable forms (Fraction 1-3) and the batch method for the insoluble forms (Fraction 4-5) was applied to the five-step SEP of Hg from CRM-JSAC0403. The sum of Hg concentrations that were classified into five fractions was in good agreement with that obtained from the batch method for all extraction, indicating that there are no significant differences in the concentrations extracted from the four channels.

Rapid and precise estimation of glycosylated serum protein (GSP) of human serum is of great importance for the treatment and diagnosis of diabetes mellitus. In this study, we propose a novel method for estimation of GSP level based on the combination of deep learning and time domain nuclear magnetic resonance (TD-NMR) transverse relaxation signal of human serum. Specifically, a principal component analysis (PCA)-enhanced one-dimensional convolutional neural network (1D-CNN) is proposed to analyze the TD-NMR transverse relaxation signal of human serum. The proposed algorithm is proved by accurate estimation of GSP level for the collected serum samples. Furthermore, the proposed algorithm is compared with 1D-CNN without PCA, long short-term memory network (LSTM) and some conventional machine learning algorithms. The results indicate that PCA-enhanced 1D-CNN (PC-1D-CNN) has the minimum error. This study proves that proposed method is feasible and superior to estimate GSP level of human serum using TD-NMR transverse relaxation signals.