Determination of photoinitiator 4-methylbenzophenone in milk by cloud point extraction

Occurrence, Fate, Human Exposure, and Toxicity of Commercial Photoinitiators

Photoinitiators (PIs) are a family of anthropogenic chemicals used in polymerization systems that generate active substances to initiate polymerization reactions under certain radiations. Although polymerization is considered a green method, its wide application in various commercial products, such as UV-curable inks, paints, and varnishes, has led to ubiquitous environmental issues caused by PIs. In this study, we present an overview of the current knowledge on the environmental occurrence, human exposure, and toxicity of PIs and provide suggestions for future research based on numerous available studies. The residual concentrations of PIs in commercial products, such as food packaging materials, are at microgram per gram levels. The migration of PIs from food packaging materials to foodstuffs has been confirmed by more than 100 reports of food contamination caused by PIs. Furthermore, more than 20 PIs have been detected in water, sediment, sewage sludge, and indoor dust collected from Asia, the United States, and Europe. Human internal exposure was also confirmed by the detection of PIs in serum. In addition, PIs were present in human breast milk, indicating that breastfeeding is an exposure pathway for infants. Among the most available studies, benzophenone is the dominant congener detected in the environment and humans. Toxicity studies of PIs reveal multiple toxic end points, such as carcinogenicity and endocrine-disrupting effects. Future investigations should focus on synergistic/antagonistic toxicity effects caused by PIs coexposure and metabolism/transformation pathways of newly identified PIs. Furthermore, future research should aim to develop "greener" PIs with high efficiency, low migration, and low toxicity.

Effective extraction of tylosin and spiramycin from fermentation broth using thermo-responsive ethylene oxide/propylene oxide aqueous two-phase systems

Recyclable aqueous two-phase systems with thermo-responsive phase-forming materials have been employed to separate macromolecules; however, these systems have achieved very limited separation efficiency for small molecules, such as antibiotics. In this study, aqueous two-phase systems composed of the ethylene oxide/propylene oxide copolymer and water were developed to extract alkaline antibiotics from the fermentation broth. In the aqueous two-phase systems with an ethylene oxide ratio of 20 and propylene oxide ratio of 80, the partition coefficients of tylosin and spiramycin reached 16.87 and 20.39, respectively, while the extraction recoveries were 70.67 and 86.70%, respectively. Coupled with mechanism analysis, we demonstrated the feasibility of extracting alkaline antibiotics using this aqueous two-phase system, especially for 16-membered macrolide antibiotics. The molecular dynamic simulation was employed to visualize the process of dual-phase formation and the partition behavior of antibiotics in an aqueous two-phase system. The dynamic simulation revealed the binding energy between the antibiotic and ethylene oxide/propylene oxide copolymers, which provides a simple indicator for screening suitable antibiotics in aqueous two-phase systems. Our recyclable aqueous two-phase systems provide a robust approach for the extraction of 16-membered macrolide antibiotics with ease of operation and high recovery rates, which is appropriate for large-scale extraction in the fermentation industry.

Preparation of multiwall carbon nanotube/urea-formaldehyde nanocomposite as a new sorbent in solid-phase extraction and its combination with deep eutectic solvent-based dispersive liquid-liquid microextraction for extraction of antibiotic residues in honey

A solid-phase extraction method combined with deep eutectic solvent-based dispersive liquid-liquid microextraction has been developed for the extraction of three antibiotics in honey samples prior to their determination by ion mobility spectrometry. In this method, first, a multiwall carbon nanotube/urea-formaldehyde nanocomposite was synthesized using co-precipitation polymerization method and then it was used as a sorbent for the analytes extraction from the samples. After that the adsorbed analytes were eluted from the sorbent using a water-miscible organic solvent. The collected elution solvent was mixed with tetrabutylammonium chloride:butanol deep eutectic solvent and the mixture was applied in the following microextraction method. The method provided low limits of detection and quantification in the ranges of 0.32-0.86 and 1.1-2.9 ng/g, respectively. The method had a proper repeatability expressed as relative standard deviation less than or equal to 9.1%. The validated method was successfully performed on different honey samples obtained from different producers.

Determination of carcinogenic herbicides in milk samples using green non-ionic silicone surfactant of cloud point extraction and spectrophotometry

A new cloud point methodology was successfully used for the extraction of carcinogenic pesticides in milk samples as a prior step to their determination by spectrophotometry. In this work, non-ionic silicone surfactant, also known as 3-(3-hydroxypropyl-heptatrimethylxyloxane), was chosen as a green extraction solvent because of its structure and properties. The effect of different parameters, such as the type of surfactant, concentration and volume of surfactant, pH, salt, temperature, incubation time and water content on the cloud point extraction of carcinogenic pesticides such as atrazine and propazine, was studied in detail and a set of optimum conditions was established. A good correlation coefficient (R² ) in the range of 0.991-0.997 for all calibration curves was obtained. The limit of detection was 1.06 µg l^-1 (atrazine) and 1.22 µg l^-1 (propazine), and the limit of quantitation was 3.54 µg l^-1 (atrazine) and 4.07 µg l^-1 (propazine). Satisfactory recoveries in the range of 81-108% were determined in milk samples at 5 and 1000 µg l^-1, respectively, with low relative standard deviation, n = 3 of 0.301-7.45% in milk matrices. The proposed method is very convenient, rapid, cost-effective and environmentally friendly for food analysis.