A simple and efficient preconcentration method based on vortex assisted reduced graphene oxide magnetic nanoparticles for the sensitive determination of endocrine disrupting compounds in different water and baby food samples by GC-FID

Graphene-based nanomaterials as potential candidates for environmental mitigation of pesticides

Over the years, bioaccumulation of hazardous chemicals in the food chain has become a critical issue, resulting in numerous health risks. Environmental mitigation aims to clean up contaminated sites and eliminate hazardous materials from the air, water, or soil to restore the site to its original and safe condition. Pesticides constitute one of the most dangerous environmental pollutants which are generally used to increase crop production. Addressing the removal or treatment of pesticides has become pivotal in mitigating environmental threats. Diverse remediation methods are employed to protect the environment and public health. Graphene-based materials have emerged as promising candidates with exceptional properties, including excellent adsorption capacity due to their high surface area, strong hydrophilicity, and tunable properties. Owing to these properties, they have been attracting major research attention in the field of design and fabrication of materials for the mitigation of pesticides from the environment such as from contaminated food, water and other samples. Various physical, chemical and biological extraction techniques are adopted to remove pesticides. This review article provides an insight into the potential role of graphene-based materials in the environmental remediation of pesticides. We have focused on the removal of Organophosphates, Organochlorines, Carbamates and Pyrethroids present in water, fruit, vegetable and other samples, highlighting the urgent need for environmental remediation. While graphene-based materials hold potential for pesticide remediation, addressing challenges in scalable production, assessing long-term sustainability, and mitigating potential environmental impacts are critical steps for successful large-scale applications.

Validation and Use of an Accurate, Sensitive Method for Sample Preparation and Gas Chromatography-Mass Spectrometry Determination of Different Endocrine-Disrupting Chemicals in Dairy Products

Endocrine disrupting chemicals (EDCs) are exogenous substances capable of altering the human hormone system and causing various diseases such as infertility and cancer as a result. In this work, a method for determining twenty-three different EDCs including parabens, alkylphenols, phenylphenols, organophosphorus pesticides, bisphenol A and triclosan in dairy products was developed. Samples are conditioned by addition of acetonitrile containing 1% formic acid, centrifugation and clean-up of the extract by continuous solid-phase extraction. EDCs in the extract are derivatised by heating in a microwave oven and quantified by gas chromatography-mass spectrometry. The proposed method features good limits of detection (6-40 ng/kg) and precision (relative standard deviation < 7.6%); also, it is scarcely subject to matrix effects (1-20%). EDC recoveries from spiked samples ranged from 80 to 108%. The method was used to analyse a total of 33 samples of dairy products including cow, sheep and goat milk, yoghourt, milkshakes, cheese, cream, butter and custard. Bisphenol A was the individual contaminant detected in the greatest number of samples, at concentrations from 180 to 4800 ng/kg. 2-Phenylphenol and ethylparaben were found in more than one-half, at concentrations over the range 130-3500 and 89-4300 ng/kg, respectively. In contrast, alkylphenols, organophosphorus pesticides and triclosan were detected in none.

Study of the Stability of Citrate Capped AgNPs in Several Environmental Water Matrices by Asymmetrical Flow Field Flow Fractionation

Asymmetrical flow field-flow fractionation (AF4) coupled to UV-Vis and dynamic light scattering (DLS) detectors in series, was tested for stability studies of dispersions of citrate-capped silver nanoparticles (AgNPs) in several water matrices. The main goal is to provide knowledge to understand their possible behavior in the environment for short times since mixturing (up to 180 min). Ultrapure (UPW), bottled (BW1, BW2), tap (TW), transitional (TrW) and sea water (SW) matrices were assayed. Observations were compatible with the aggregation of AgNPs, a change in the plasmon band and a size growth with time were done. Fractograms showed different evolution fingerprints in the function of the waters and batches. The aggregation rate order was BW2, SW, TrW, BW1 and TW, being BW2 the lowest and TW the highest. NP aggregation can be induced by increasing the salt concentration of the medium, however transitional and sea waters did not follow the rule. Both matrices presented a lower aggregation rate in comparison with other aqueous matrices with much lower ionic strength (BW1 and TW), which can be explained by the potential presence of dissolved organic matter and/or the high concentration of halides providing their stabilization and passivation, respectively. AF4 provides relevant information with respect to static DLS and UV-Vis Spectroscopy showing that at least two populations of aggregates with different sizes between them, depending on both, the mixture time for a given matrix and type of water matrix for the same time.

Concentration-Emission Matrix (CEM) Spectroscopy Combined with GA-SVM: An Analytical Method to Recognize Oil Species in Marine

The establishment and development of a set of methods of oil accurate recognition in a different environment are of great significance to the effective management of oil spill pollution. In this work, the concentration-emission matrix (CEM) is formed by introducing the concentration dimension. The principal component analysis (PCA) is applied to extract the spectral feature. The classification methods, such as Probabilistic Neural Networks (PNNs) and Genic Algorithm optimization Support Vector Machine (SVM) parameters (GA-SVM), are used for oil identification and the recognition accuracies of the two classification methods are compared. The results show that the GA-SVM combined with PCA has the highest recognition accuracy for different oils. The proposed approach has great potential in rapid and accurate oil source identification.