Investigation of ICP-MS/MS for total sulfur quantification in freshwater dissolved organic matter

Releases of microplastics and chemicals from nonwoven polyester fabric-based polyurethane synthetic leather by photoaging

The nonwoven PET fabrics are chemically, mechanically and thermally treated fiber aggregate without weaving, knitting or braiding, which could be used as a base to make polyurethane (PU) synthetic leather through a series of processing. Our research systematically compared the photoaging behaviors of pure non-woven PET base fabric (NPET-P) and PU synthetic leather (nonwoven PET-base fabrics with PU coating, NPET-U), and their possibilities for microplastic fibers (MPFs) generation and chemical transformation in water. NPET-U was photoaged to a higher oxidation degree with higher O/C ratios and more distinct changes in chemical structures. The amount of MPFs released from NPET-U (1.98 × 107 g/fibers) was significantly lower than that from NPET-P (4.76 × 107 g/fibers) after 360 h light irradiation (p value <0.05) with a slower degradation rate and delayed MPFs release. The lengths and diameters of released MPFs from NPET-U varied within a smaller range than that from NPET-P exposed to UV light irradiation. Natural sunlight aging of fabrics for 365 days was found to be equivalent to approximately 85.3-127.2 h UV aging in the laboratory, which indicated the lab accelerated experiments was extraordinarily intense to simulate natural sunlight aging. Furthermore, abundant calcium and sulfur-contained chemicals were detected in original fabrics and the leachate of 360 h light-aged fabrics using the inductively coupled plasma optical emission spectrometer (ICP-OES). The organic components of the leachate were separated according to their molecular weight with the changes of dissolved organic carbon (DOC), dissolved organic nitrogen (DON), and the UV response over aging time. UV stimulation aggravated the role of plastic polymers as disinfection by-product (DBP) precursors. Nevertheless, although NPET-U could produce more nitrogen-contained chemicals, it had similar formation potentials of nitrogen-containing DBPs as NPET-P. The discussion lucubrated the potential risks of the production of MPFs and chemical release in the leachate with regard to combined plastic pollution.

Machine Learning System To Monitor Hg2+ and Sulfide Using a Polychromatic Fluorescence-Colorimetric Paper Sensor

An optical monitoring device combining a smartphone with a polychromatic ratiometric fluorescence-colorimetric paper sensor was developed to detect Hg2+ and S2- in water and seafood. This monitoring included the detection of food deterioration and was made possible by processing the sensing data with a machine learning algorithm. The polychromatic fluorescence sensor was composed of blue fluorescent carbon quantum dots (CDs) (BU-CDs) and green and red fluorescent CdZnTe quantum dots (QDs) (named GN-QDs and RD-QDs, respectively). The experimental results and density functional theory (DFT) prove that the incorporation of Zn can improve the stability and quantum yield of CdZnTe QDs. According to the dynamic and static quenching mechanisms, GN-QDs and RD-QDs were quenched by Hg2+ and sulfide, respectively, but BU-CDs were not sensitive to them. The system colors change from green to red to blue as the concentration of the two detectors rises, and the limits of detection (LOD) were 0.002 and 1.488 μM, respectively. Meanwhile, the probe was combined with the hydrogel to construct a visual sensing intelligent test strip, which realized the monitoring of food freshness. In addition, a smartphone device assisted by multiple machine learning methods was used to text Hg2+ and sulfide in real samples. It can be concluded that the fabulous stability, sensitivity, and practicality exhibited by this sensing mechanism give it unlimited potential for assessing the contents of toxic and hazardous substances Hg2+ and sulfide.