Synthesis of imidazolium ionic liquid immobilized on magnetic mesoporous silica: A sorbent material in a green micro-solid phase extraction of multiclass pesticides in water.

In this study, we synthesized an imidazolium ionic liquid immobilized on magnetic mesoporous silica (IL-MMS) and evaluated its performance as a sorbent material for a green micro-solid phase extraction (µ-SPE) of multiclass pesticides in water. The synthesized IL-MMS was characterized by various analytical techniques, including Fourier-transform infrared spectroscopy (FTIR), Brunauer-Emmett-Teller (BET) and Barrett-Joyner-Halenda (BJH) analyses (N2 adsorption/desorption), Vibrating Sample Magnetometer (VSM), energy-dispersive spectroscopy (EDS) and Field emission scanning electron microscopy (FESEM). Our synthesized IL-MMS demonstrated excellent magnetic properties (31.5 emu/g), high surface area (1177.4 m2/g), proper pore size (⁓4.2 nm) and volume (1.80 cm3/g). Under optimized extraction conditions, the IL-MMS exhibited a high adsorption capacity for a variety of pesticides, including organophosphates, carbamates, and pyrethroids. The proposed µ-SPE method using IL-MMS showed good linearity (R2 > 0.99), low limits of detection (LODs) ranging from 0.04 to 1.63 ng/L, and suitable recovery rate was between 82.4% and 109.8% for different pesticides. In addition, the method also exhibited excellent reproducibility, with relative standard deviations (RSDs) of less than 8% for both intra and inter-day precision. In overall, the synthesized IL-MMS has proven to be a highly promising material for sorbent-based micro-solid phase extraction (µ-SPE) of multiclass pesticides in water. With its simple, efficient, and eco-friendly approach to pesticide analysis, this method shows great potential for future pesticide detection and monitoring efforts due to its sensitivity, accuracy, and adaptability to various environmental conditions.

Hollow mesoporous silica nanoparticles: Effective silica etching using tri-di- and mono-valent cations.

Among different hollow nanostructures, the preparation of hollow mesoporous silica nanoparticles (HMSNs) is still a hotspot research field due to their unique properties e.g., large pore sizes and volumes, high drug loading capacity, ease of surface modification, large surface area, and biodegradability. Herein, novel uniform HMSNs are prepared for the first time by a combination of heterogeneous oil-water biphase stratification and simple mono-, di-, and tri-valent etching reactions. The biphase stratification reaction allows self-assembly of reactants at the oil-water interface, while the subsequent step is designed for the efficient selective silica etching under mild conditions. We have studied the effect of cation's valence (NH4+, Ca2+, and Al3+) on the silica etching reaction coupled with the biphase stratification reaction both in the absence and presence of the auxiliary pore expanded agent 1, 3, 5 trimethylbenzene (TMB). In the absence of TMB, the Brunauer-Emmett-Teller (BET) analysis confirms that Al3+ creates materials with the largest pore size (18.0 nm), whereas the use of NH4+ results in the largest pore volume (2.30 cm3/g). The pores generated using Ca2+ and Al3+ as silica etching agents have a volume 2.01 cm3/g and 2.05 cm3/g, respectively. Similar experiments in the presence of TMB leads to the formation of HMSN with larger pore sizes (24 nm and 21.5 nm) and volumes (2.70 cm3/g and 2.12 cm3/g) when using Al3+ and Ca2+, respectively, as etching agents. Drug loading capacity using Langmuir adsorption model indicate our hollow MSN material exhibit the high adsorbing DOX up to 558.23 mg per gram of nanoparticles in pH of 7.2. Furthermore, synthetized NPs exhibited high loading capacity for large protein and biomolecules such as BSA. Our findings confirmed that the charge density of cation has a critical role on selective silica etching in the preparation of HMSNs.

Green and simple synthesis route of Ag@AgCl nanomaterial using green marine crude extract and its application for sensitive and selective determination of mercury.

Advanced exploitation in the green synthesis of nanomaterials has received considerable attention in the recent years. So that, an eco-friendly approach is proposed for the synthesis of silver­silver chloride nanoparticles (Ag@AgCl-NPs) which does not require any external reducing & capping agents, organic solvent and external halide sources using an aqueous extract green marine alga (Chaetomorpha sp).In order to characterize the formation of Ag@AgCl-NPs, several instruments including UV-vis, FTIR, HR-TEM, EDS mapping, XRD, XPS, SAED and DLS were used. On the other hands, although numerous methods have been reported for the analysis of toxic Hg2+ in drinking water, development of simple, rapid, inexpensive, selective and sensitive sensors still remains a great challenge. Herein, the colorimetric sensor studies of this green synthesized Ag@AgCl-NPs showed an interesting feature for sensing of hazardous Hg2+ in water. The colorimetric assay is based on the concentration - dependent degradation of as-prepared Ag@AgCl-NPs in the presence of Hg2+. The detection limit of this affordable assay is 4.19 nM which is below the defined value by china agency and more importantly is below the defined by the U.S. Environmental Protection Agency for drinkable water.

Development of a new colorimetric assay for detection of bisphenol-A in aqueous media using green synthesized silver chloride nanoparticles: experimental and theoretical study.

In the present study, a cost-effective, green and simple synthesis method was applied for preparation of stable silver chloride nanoparticles (AgCl-NPs). The method was done by forming AgCl-NPs from Ag+ ions using aqueous extract of brown algae (Sargassum boveanum) obtained from the Persian Gulf Sea. This extract served as capping agent during the formation of AgCl-NPs. Creation of AgCl-NPs was confirmed by UV-visible spectroscopy, powder X-ray diffraction, energy-dispersive X-ray spectroscopy, and high-resolution transmission electron microscopy, while the morphology and size analyses were characterized using high-resolution transmission electron microscopy and dynamic light scattering. After optimization of some experimental conditions, particularly pH, a simple and facile system was developed for the naked-eye detection of bisphenol-A. Moreover, a theoretical study of AgCl interaction with bisphenol-A was performed at the density functional level of theory in both gas and solvent phases. Theoretical results showed that electrostatic and van der Waal interactions play important roles in complexation of bisphenol-A with AgCl-NPs, which can lead to aggregation of the as-prepared AgCl-NPs and results in color change from specific yellow to dark purple, where a new aggregation band induced at 542 nm appears. The absorbance at 542 nm was found to be linearly dependent on the bisphenol-A concentration in the range of 1 × 10-6-1 × 10-4 M, with limit of detection of 45 nM. In conclusion, obtained results from the present study can open up an innovative application of the green synthesis of AgCl-NPs using brown algae extract as colorimetric sensors.

Variable selection in multivariate calibration based on clustering of variable concept.

Recently we have proposed a new variable selection algorithm, based on clustering of variable concept (CLoVA) in classification problem. With the same idea, this new concept has been applied to a regression problem and then the obtained results have been compared with conventional variable selection strategies for PLS. The basic idea behind the clustering of variable is that, the instrument channels are clustered into different clusters via clustering algorithms. Then, the spectral data of each cluster are subjected to PLS regression. Different real data sets (Cargill corn, Biscuit dough, ACE QSAR, Soy, and Tablet) have been used to evaluate the influence of the clustering of variables on the prediction performances of PLS. Almost in the all cases, the statistical parameter especially in prediction error shows the superiority of CLoVA-PLS respect to other variable selection strategies. Finally the synergy clustering of variable (sCLoVA-PLS), which is used the combination of cluster, has been proposed as an efficient and modification of CLoVA algorithm. The obtained statistical parameter indicates that variable clustering can split useful part from redundant ones, and then based on informative cluster; stable model can be reached.

Identification of discriminatory variables in proteomics data analysis by clustering of variables.

This article presents a data analysis method for biomarker discovery in proteomics data analysis. In factor analysis-based discriminate models, the latent variables (LV's) are calculated from the response data measured at all employed instrument channels. Since some channels are irrelevant and their responses do not possess useful information, the extracted LV's possess mixed information from both useful and irrelevant channels. In this work, clustering of variables (CLoVA) based on unsupervised pattern recognition is suggested as an efficient method to identify the most informative spectral region and then it is used to construct a more predictive multivariate classification model. In the suggested method, the instrument channels (m/z value) are clustered into different clusters via self-organization map. Subsequently, the spectral data of each cluster are separately used as the input variables of classification methods such as partial least square-discriminate analysis (PLS-DA) and extended canonical variate analysis (ECVA). The proposed method is evaluated by the analysis of two experimental data sets (ovarian and prostate cancer data set). It is found that our proposed method is able to detect cancerous from healthy samples with much higher sensitivity and selectivity than conventional PLS-DA and ECVA methods.

Discrimination of edible oils and fats by combination of multivariate pattern recognition and FT-IR spectroscopy: a comparative study between different modeling methods.

By using FT-IR spectroscopy, many researchers from different disciplines enrich the experimental complexity of their research for obtaining more precise information. Moreover chemometrics techniques have boosted the use of IR instruments. In the present study we aimed to emphasize on the power of FT-IR spectroscopy for discrimination between different oil samples (especially fat from vegetable oils). Also our data were used to compare the performance of different classification methods. FT-IR transmittance spectra of oil samples (Corn, Colona, Sunflower, Soya, Olive, and Butter) were measured in the wave-number interval of 450-4000 cm(-1). Classification analysis was performed utilizing PLS-DA, interval PLS-DA, extended canonical variate analysis (ECVA) and interval ECVA methods. The effect of data preprocessing by extended multiplicative signal correction was investigated. Whilst all employed method could distinguish butter from vegetable oils, iECVA resulted in the best performances for calibration and external test set with 100% sensitivity and specificity.