Multivariate optimization of a dispersive liquid-liquid microextraction method for the determination of six antiparasite drugs in kennel effluent waters by using second-order chromatographic data

Removal and Recovery of AgNPs from Water by Sustainable Magnetic Nanoflocculants

The presence of silver nanoparticles (AgNPs) in water bodies has emerged as a new environmental concern and the efficient separation of these nanoparticles remains a critical challenge. Here, we developed novel magnetic nanoflocculants for the recovery of AgNPs from water. Alternating layers of biopolymers, in particular, chitosan, alginate, and polymeric bio-based soluble substances (BBS) derived from urban waste, were coated on magnetic nanoparticles via the layer-by-layer technique to prepare reusable magnetic nanoflocculants (MNFs). The MNFs obtained were characterized with diverse physicochemical techniques. Surface response methodology, based on the Doehlert matrix, has shown to be a useful tool to determine the effect of pH (in the range 5-9), concentration of AgNPs (7-20 mg L-1), and MNFs (50-1000 mg L-1) on the performance of AgNPs removal. The model predicts a high AgNPs removal percentage at low pH values and high MNF concentration. In particular, for the most efficient MNFs, 90% of AgNPs removal was obtained at pH 5 and 600 mg L-1 MNF concentration. Additionally, the effects of AgNPs size, ionic strength, the presence of humic acids, and two types of surfactants (LAS anionic and TWEEN 20 nonionic) on the AgNPs removal were evaluated. Finally, recovery and reuse experiments showed that MNF made of Chitosan-BBS can be reused in ten cycles, losing only 30% of the initial removal capacity. Therefore, magnetic flocculation could represent a sustainable alternative for AgNPs separation with potential applications in water treatment and remediation of nanoparticle contamination.

Development of a dispersive liquid-liquid microextraction technique for ultra-sensitive detection of gold in environmental samples using atomic absorption spectrometry

The work examines the determination of gold from environmental samples by dispersive liquid-liquid microextraction (DLLME) method. The method was developed for the separation and determination of Au (III) ions after chelating with bis (salicylaldehyde) ethylenediimine (H2SA2en) Schiff-base as derivatizing reagent. Flame atomic absorption spectrometry (FAAS) and inductively coupled plasma-optical emission spectrometry (ICP-OES) techniques were used for quantitation of Au (III). These techniques are sensitive and rapid for the determination of gold concentrations in ore, water and sediment samples. The influence of factors such as pH, reagent concentration, solvents (extracting) (disperser) and solvent volumes on extraction efficiency of Au (III) ions were studied and optimized by univariate and multivariate techniques. The linearity of the method was in the range of 2 to 12 µg/L with R2 = 0.997. The limit of detection was 1 µg/L, and the limit of quantification was 3 µg/L. The preconcentration factor and enrichment factor values were 44 and 47. The repeatability (the intra-day) and reproducibility (the inter-day) precisions (n = 3) were found to be 0.417-3.56%. The proposed method was successfully applied for the determination of gold in sediment samples of the Indus River, Kori Barrage, goldsmith water, acidic solution of goldsmith and ornament samples collected from Goldsmith Labs and shops. The results found from FAAS were compared with those obtained from ICP-OES technique, and a good correlation with comparable selectivity and sensitivity was specified.

Applications of artificial intelligence for chemical analysis and monitoring of pharmaceutical and personal care products in water and wastewater: A review

Specifying and interpreting the occurrence of emerging pollutants is essential for assessing treatment processes and plants, conducting wastewater-based epidemiology, and advancing environmental toxicology research. In recent years, artificial intelligence (AI) has been increasingly applied to enhance chemical analysis and monitoring of contaminants in environmental water and wastewater. However, their specific roles targeting pharmaceuticals and personal care products (PPCPs) have not been reviewed sufficiently. This review aims to narrow the gap by highlighting, scoping, and discussing the incorporation of AI during the detection and quantification of PPCPs when utilising chemical analysis equipment and interpreting their monitoring data for the first time. In the chemical analysis of PPCPs, AI-assisted prediction of chromatographic retention times and collision cross-sections (CCS) in suspect and non-target screenings using high-resolution mass spectrometry (HRMS) enhances detection confidence, reduces analysis time, and lowers costs. AI also aids in interpreting spectroscopic analysis results. However, this approach still cannot be applied in all matrices, as it offers lower sensitivity than liquid chromatography coupled with tandem or HRMS. For the interpretation of monitoring of PPCPs, unsupervised AI methods have recently presented the capacity to survey regional or national community health and socioeconomic factors. Nevertheless, as a challenge, long-term monitoring data sources are not given in the literature, and more comparative AI studies are needed for both chemical analysis and monitoring. Finally, AI assistance anticipates more frequent applications of CCS prediction to enhance detection confidence and the use of AI methods in data processing for wastewater-based epidemiology and community health surveillance.

Recent Advances in Dispersive Liquid-Liquid Microextraction for Pharmaceutical Analysis

Sample clean-up and pre-concentration are critical components of pharmaceutical analysis. The dispersive liquid-liquid microextraction (DLLME) technique is widely recognized as the most effective approach for enhancing overall detection sensitivity. While various DLLME modes have been advanced in pharmaceutical analysis, there need to be more discussions on pre-concentration techniques specifically developed for this field. This review presents a comprehensive overview of the different DLLME modes used in pharmaceutical analysis from 2017 to May 2023. The review covers the principles of DLLME, the factors affecting microextraction, the selected applications of different DLLME modes, and their advantages and disadvantages. Additionally, it focuses on multi-extraction strategies employed for pharmaceutical analysis.

Anthelmintics in the environment: Their occurrence, fate, and toxicity to non-target organisms

Anthelmintics are drugs used for the treatment and prevention of diseases caused by parasitic worms (helminths). While the importance of anthelmintics in human as well as in veterinary medicine is evident, they represent emerging contaminants of the environment. Human anthelmintics are mainly used in tropical and sub-tropical regions, while veterinary anthelmintics have become frequently-occurring environmental pollutants worldwide due to intensive agri- and aquaculture production. In the environment, anthelmintics are distributed in water and soil in relation to their structure and physicochemical properties. Consequently, they enter various organisms directly (e.g. plants, soil invertebrates, water animals) or indirectly through food-chain. Several anthelmintics elicit toxic effects in non-target species. Although new information has been made available, anthelmintics in ecosystems should be more thoroughly investigated to obtain complex knowledge on their impact in various environments. This review summarizes available information about the occurrence, behavior, and toxic effect of anthelmintics in environment. Several reasons why anthelmintics are dangerous contaminants are highlighted along with options to reduce contamination. Negative effects are also outlined.

A new constraint to model background signals when processing chromatographic-spectral second-order data with multivariate curve resolution

BACKGROUND

the chemometric processing of second-order chromatographic-spectral data is usually carried out with the aid of multivariate curve resolution-alternating least-squares (MCR-ALS). When baseline contributions occur in the data, the background profile retrieved with MCR-ALS may show abnormal lumps or negative dips at the position of the remaining component peaks.

RESULTS

The phenomenon is shown to be due to remaining rotational ambiguity in the obtained profiles, as confirmed by the estimation of the boundaries of the range of feasible bilinear profiles. To avoid the abnormal features in the retrieved profile, a new background interpolation constraint is proposed and described in detail. Both simulated and experimental data are employed to support the need of the new MCR-ALS constraint. In the latter case, the estimated analyte concentrations agreed with those previously reported.

SIGNIFICANCE

The developed procedure helps to reduce the extent of rotational ambiguity in the solution and to better interpret the results on physicochemical grounds.

Vortex-assisted supramolecular solvent dispersive liquid–liquid microextraction of ketoprofen and naproxen from environmental water before chromatographic analysis: response surface methodology optimisation

A microextraction procedure that is rapid and simple to extract and preconcentrate ketoprofen and naproxen is proposed. An environmentally friendly supramolecular solvent was applied as an extraction solvent and proved to be efficient in the extraction of ketoprofen and naproxen from environmental water. The design of experiment approach was used to screen, optimize significant parameters, and determine optimum experimental conditions. Under optimized experimental conditions, the vortex-assisted supramolecular solvent dispersive liquid–liquid microextraction provided a good linearity (0.57–700 µg L−1), low limits of detection (0.17–0.24 µg L−1) and extraction reproducibility below 9%. The high percentage relative recoveries (93.6–101.4%) indicated that the method is not affected by matrix. The practical applicability of the method was assessed by analysing ketoprofen and naproxen in river water and effluent wastewater samples. Both analytes were found in effluent wastewater.

Strategies of dispersive liquid-liquid microextraction for coastal zone environmental pollutant determination

Coastal zone means the interface of land and sea, and therefore, environmental pollutants steaming from land-based activities (like manufactories) and sea-based activities (like shipping) are all existing in coastal zone. These pollutants usually have characteristics of low residues, complicated matrices, easy accumulation and so on, causing difficulty to detect coastal pollutants quickly and sensitively. It is imperative to perform effective sample preparation prior to instrumental analysis. Dispersive liquid-liquid microextraction (DLLME) has attracted significant research interest for sample preparation, owing to its high enrichment ability, low reagent/sample consumption, and wide analyte/matrix applicability, as well as robustness, simplicity, rapidity and inexpensiveness. Herein, we comprehensively review the recent advancements of DLLME technology and its analytical parameters including enrichment principles, extraction modes, and practical application; the emphasis is on novel mode-construction and representative coastal-environmental pollutants extraction. Construction strategies are highlighted by classifying DLLME into five major modes, according to extractant's types, including normal ones, low density solvents, ionic liquids, deep eutectic solvents and others. The coupling of DLLME with other extraction techniques like solid-phase extraction is also briefly introduced. The strengths and weaknesses of each strategy and its rationality are also elaborated. In addition, some typical applications of the different DLLME modes for the determination of organic compounds and heavy metals in coastal water, sediment, soil, and biota are summarized. The increasingly concerned green aspects and instrumentation of DLLME are presented, and finally, the challenges and perspectives of the DLLME for environmental analysis are proposed.

Chromatographic Applications in the Multi-Way Calibration Field

In this review, recent advances and applications using multi-way calibration protocols based on the processing of multi-dimensional chromatographic data are discussed. We first describe the various modes in which multi-way chromatographic data sets can be generated, including some important characteristics that should be taken into account for the selection of an adequate data processing model. We then discuss the different manners in which the collected instrumental data can be arranged, and the most usually applied models and algorithms for the decomposition of the data arrays. The latter activity leads to the estimation of surrogate variables (scores), useful for analyte quantitation in the presence of uncalibrated interferences, achieving the second-order advantage. Recent experimental reports based on multi-way liquid and gas chromatographic data are then reviewed. Finally, analytical figures of merit that should always accompany quantitative calibration reports are described.

Proof of the environmental circulation of veterinary drug albendazole in real farm conditions

Anthelmintics, drugs against parasitic worms, are frequently used in livestock and might act as danger environmental microcontaminants. The present study was designed to monitor the possible circulation of common anthelmintic drug albendazole (ABZ) and its metabolites in the real agriculture conditions. The sheep were treated with the recommended dose of ABZ. Collected faeces were used for the fertilization of a field with fodder plants (alfalfa and clover) which served as feed for sheep from a different farm. The selective ultrasensitive mass spectrometry revealed surprisingly high concentrations of active ABZ metabolite (ABZ-sulphoxide) in all samples (dung, plants, ovine plasma, rumen content and faeces). Our results prove for the first time an undesirable permeation of ABZ metabolites from sheep excrement into plants (used as fodder) and subsequently to other sheep in real agricultural conditions. This circulation causes the permanent exposition of the ecosystems and food-chain to the drug and can promote the development of drug resistance in helminths.

Mathematical Modelling of Biosensing Platforms Applied for Environmental Monitoring

In recent years, mathematical modelling has known an overwhelming integration in different scientific fields. In general, modelling is used to obtain new insights and achieve more quantitative and qualitative information about systems by programming language, manipulating matrices, creating algorithms and tracing functions and data. Researchers have been inspired by these techniques to explore several methods to solve many problems with high precision. In this direction, simulation and modelling have been employed for the development of sensitive and selective detection tools in different fields including environmental control. Emerging pollutants such as pesticides, heavy metals and pharmaceuticals are contaminating water resources, thus threatening wildlife. As a consequence, various biosensors using modelling have been reported in the literature for efficient environmental monitoring. In this review paper, the recent biosensors inspired by modelling and applied for environmental monitoring will be overviewed. Moreover, the level of success and the analytical performances of each modelling-biosensor will be discussed. Finally, current challenges in this field will be highlighted.