Water-resistant MOF-235 was used to adsorb N,N-dimethylamine (DMA) from aqueous solutions. It was synthesized from terephthalic acid and FeCl₃.6H₂O under air-free conditions and characterized by its crystalline structure, functional groups and temperature resistance. The kinetic data results were best adjusted to the pseudo-second order model (R²>0.963). The best-fit isotherm, Langmuir model, suggested the adsorption of DMA is localized on homogenously distributed active sites on the surface. This fit was confirmed by the value of β = 1 on the Redlich-Peterson model. Our study suggests that the manipulation of novel materials such as MOF-235 promises new avenues for water treatment solutions. Schematic structure of MOF-235 and its application as adsorbent. Graphical Abstract

Pisco is an alcoholic beverage obtained from grape juice distillation. Considered the flagship drink of Peru, it is produced following strict and specific quality standards. In this work, sensing results for volatile compounds in pisco, obtained with an electronic nose, were analyzed through the application of machine learning algorithms for the differentiation of pisco varieties. This differentiation aids in verifying beverage quality, considering the parameters established in its Designation of Origin”. For signal processing, neural networks, multiclass support vector machines and random forest machine learning algorithms were implemented in MATLAB. In addition, data augmentation was performed using a proposed procedure based on interpolation–extrapolation. All algorithms trained with augmented data showed an increase in performance and more reliable predictions compared to those trained with raw data. From the comparison of these results, it was found that the best performance was achieved with neural networks.

Activated carbons (ACs) were prepared from aguaje seeds at different impregnation ratios of \({H_{3}PO_{4}}\)/precursor equivalent to 0.5, 0.75, 1.0 and 1.5, for the adsorption of N,N-dimethylamine (DMA). The samples were characterized by \(N_2\)-sorption (\(S_{BET}\)), X-ray diffraction, infrared spectroscopy (FTIR), Boehm titration, scanning electron microscopy and energy-dispersive X-ray spectroscopy. The samples were successfully activated to obtain highly microporous surface (>95 %) with high total BET surface area (\(>\)695 \(\mathrm{m^{2}\,g}^{-1}\)). Common acid functional groups were determined by the Boehm titration and FTIR. The kinetic data revealed that the results were better adjusted to pseudo-second order and Elovich models. To predict the adsorption process, different isotherm models were tested. The best fit isotherm model for AC-0.5 and AC-0.75 samples was the Temkin isotherm model. Meanwhile, the Langmuir isotherm was the best fit model for AC-1.0 and AC-1.5 samples. Moreover, it was found that the adsorption of DMA was highly influenced by the surface acidity and the microporosity of the carbons.

There is great interest in the development of a simple system that could identify adulteration or counterfeiting of Peruvian Pisco (a grape-based alcoholic drink). In this study, sensors based on SnO₂-TiO₂ and SnO₂-MoO₃ composites with different weight composition ratios were synthesized and characterized. These sensors were tested with aqueous solutions of EtOH/MeOH and Pisco samples of Italia and Quebranta varieties in order to explore their capacity to identify variations in these beverages. The response profile of the most sensitive sensors showed an enhanced response to alcoholic samples with greater content of ethanol up to a concentration of 45%v/v, while the increased content of methanol in the range of 0.1 to 0.3% v/v diminished the intensity of the sensor response. Differences in the composition of methanol and ethanol in the Pisco varieties studied (Italia and Quebranta) were correlated to the capacity of the composite-based sensors to differentiate them with greater performance. Sensors based on SnO₂-TiO₂-1/2 composites showed greater reproducibility in their response profile over time in comparison to SnO₂-TiO₂-1/1 and SnO₂-MoO₃ composites. The PCA showed that composite sensors were able to differentiate Pisco samples according to the varieties, Italia and Quebranta.

This study focuses on the synthesis and characterization of metal oxides doped with transition metals with the purpose of enhancing their sensitivity to detect organophosphate insecticides, Chlorpyrifos and Malathion, on air. The moderate toxicity of these organophosphorus pesticides and their extensive use in small-scale agriculture reveals the urgency of continuous air quality monitoring in the work environments of communities with restricted access to sophisticated techniques for detecting these contaminants, such as high-performance liquid chromatography (HPLC) and gas chromatography-mass spectrometry (GC-MS). The selected metal oxides are Zr doped zinc oxide (ZnO) and tin oxide (SnO₂) doped with both Pt and Zr. These semiconductors (ZnO and SnO₂) have the advantage of showing a sensing response at low working temperatures, in the range of 200°C and 250°C. Additionally, both have great thermal and chemical stability. The synthesis of ZnO is performed through the application of a hydrothermal synthesis methodology in an autoclave; while ZnO doping is conducted by adding the dopant precursor (ZrOCl₂) to the reaction mixture. In the case of SnO₂, commercial tin oxide is used for doping with Zr and Pt. The doping process was carried out in two separate processes: 1) mechanical mixing and sintering of ZrO₂ and SnO_2, and 2) chemical reduction of H₂Cl₆Pt with SnSO₄ on the surface of SnO₂. The characterization of these materials is carried out by employing X-ray diffraction (DRX), Fourier-transformed infrared spectroscopy (FTIR), ultraviolet-visible spectroscopy (UV-Vis), scanning electron microscopy / energy dispersive X-ray spectroscopy (SEM/EDS), transmission electron microscopy (TEM) and sorption of N₂ analysis. In addition, the quantification of pesticide concentration in commercial samples, that are used in the sensing essays, is performed with HPLC. The Pt doped SnO₂ is prepared with a content of Pt between 0.05 and 0.2 wt. %, while the concentration of Zr is in the range of 0.3 and 0.7 wt. %. In the case of ZnO, the concentration of Zr is between 0.9 and 2.0 wt. %. The characterization by DRX reveals a substitutional doping for both materials. Additionally, the dopants produce an increase of the lattice strain and a small contraction of the unit cell in both metal oxides. The SEM analysis of Zr doped ZnO allows to identify changes in their morphology, which confirms an increase in particle size at Zr concentration of 2.0 wt. % in comparison to 1.2 wt. %. The sensing tests of the doped metal oxides are evaluated in an electronic nose. This equipment allows to test four sensors each time and the vapors of the pesticides are produced through a bubbling process of a liquid suspension of one organophosphate insecticide in water. The prepared doped oxides present an enhanced sensibility for the detection of organophosphates on air. Response signals are obtained with greater stability for long sensing times with zinc oxide sensors doped with Zr at 2.0 wt. % in comparison to sensors of Zr doped ZnO with lower Zr content. A study of the optimum temperature conditions (between 210°C and 220°C) and concentration of the dopant in zinc oxide and tin oxide is carried out. The temperature has a positive effect on the sensitivity of all the sensors tested. At the test temperature of 220°C, the sensitivity of the best sensors (AT-Zr-2.0-ZnO and Pt-0.13-ZrO₂-0.15-SnO₂) for Malathion can be maximized. In the case of Chlorpyrifos, better results are obtained for the Pt-0.13-ZrO₂-0.15-SnO₂ sensor, whose signal is favored at 210°C. In addition, the sensitivity of Pt-0.13-ZrO₂-0.15-SnO₂ with respect to AT-Zr-2.0-ZnO at 220°C is higher for Chlorpyrifos, but lower for Malathion. The statistical treatment method of principal component analysis (PCA) allows the evaluation of the signals of samples with different concentrations of pesticides. The best PCA, obtained with the data of the measurements using the most sensitive sensors, show a total explained variance greater than 90% and a better differentiation between air samples contaminated with pesticides and air samples without contamination. Figure 1