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Hyperspectral imaging was used for large-scale monitoring of the residual moisture in wide textile webs at the end of the drying process that follows their washing or finishing by impregnation in aqueous solutions or dispersions. Such data are essential for optimizing the energy efficiency and the precise control of the drying process. Quantitative analysis of the recorded spectral data was carried out with multivariate regression methods such as the partial least squares (PLS) algorithm. Reference data for calibration of the prediction models were determined by gravimetry. The drying of textile materials from both natural or synthetic fibers possessing different water absorption capacities (cotton, polyamide, polyester), which were partially finished with an optical brightener, was investigated. Moisture contents in the range from 0 to about 12 wt% were considered in the calibration models. For all systems, the root mean square error of prediction (RMSEP) for the residual moisture was found to be about 0.5 wt%, that is, about 1 g/m2. In addition to the quantitative determination of the water content, hyperspectral imaging provides detailed information about its spatial distribution across the textile web, which may help to improve the control of the drying process. In particular, it was demonstrated that the developed methods were capable of detecting and visualizing inhomogeneous moisture distributions. Averaging of the individual values of the moisture content predicted from all spectra across the surface of the textile samples resulted in a very close correlation with the corresponding gravimetric reference values. Due to the averaging process, the difference between both values is generally lower than RMSEP even in case of samples with inhomogeneous distribution of the moisture. The high precision and the broad capabilities of the developed analytic methods for in-line monitoring of the moisture content hold the potential for an efficient process control in technical textile converting processes.Carbon paste electrode (CPE) modified with porous copper based metal organic framework (Cu-MOF) nanocomposite is described for analysis of cyanide (CN-) for the first time. The electrochemical performance of the proposed electrode was investigated by differential pulse voltammetry (DPV), electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV). The Cu-MOF nanocomposite was characterized using scanning electron microscope (SEM), N2-adsorption-desorption isotherms, powder X-ray powder diffraction (PXRD), Fourier transform infrared spectroscopy (FTIR) and thermogravimetric analysis (TGA). Under optimal conditions of measurements, the anodic peak (Ipa) decreases linearly in the range of 1.87-25 μM with LOD of 0.60 μM (at S/N = 3). The Cu-MOF/CPE showed good selectivity towards CN- measurement with no significant interference in pH 7.0 using 0.25 M KCl to increase the medium conductivity and to stabilize the analyte and prevents its volatility. Moreover, the method was successfully applied for determination of CN- in different environmental water samples.In a novel approach, an efficient non-enzymatic glucose sensor based on pure phase of aurivillius bismuth molybdate (BM or γ-Bi2MoO6) mixed metal oxides is reported. Three BM samples were synthesized, with/without l-cysteine (Cys) and dodecylamine (DDA) as additives, leading to different shapes bullet (BM-C), confetti (BM-2Cys) and candy (BM-2DDA). The morphology and purity of the structures were confirmed by FE-SEM images and XRD. In order to investigate the sensor application, the samples were integrated on reduced graphene oxide and incorporated into simple and inexpensive glassy carbon electrode (GCE) without using any polyvinylpyrrolidone (PVP) or Nafion. To perform cyclic voltammetry experiments, all three biosensors were measured in PBS solution (pH = 7) in ±1.5 voltage range and 50 mV s-1 scan rate. Glucose identification by the synthesized composites is an obvious sign of their high efficiency. According to chronoamperomograms, the best sensitivity of 3050 μA L mmol-1 cm-2 with linear range of 0.02-0.14 mmol L-1, low detection limit (LOD) of 0.004 mmol L-1 and the signal/noise equal to 3 was achieved by BM-2DDA/rGO/GCE biosensor and its speedy amperometric response is less than 5 s. This biosensor showed impressive selectivity, repeatability and reproducibility results besides it maintains its stability considerably in great percentage of 98.5% after eight weeks. [https://www.selleckchem.com/products/cc-92480.html learn more] Also it showed prolonged stability after 50 min.Pesticides can have harmful effects on the environment and on humans, resulting in acute, or in some cases, fatal poisoning. Pesticides are inexpensive, easily accessible, and commonly linked to forensic investigations involving suicide or attempted suicide. Pesticide exposure is monitored by determining the presence of the pesticide (or its metabolites) in biological samples, such as blood serum. Traditional methods require the use of a large sample volume and extensive sample preparation to confirm the presence of such harmful materials. Thus, owing to their unique physico-chemical properties, metal ferrites nanoparticles (NPs) were developed as assisting agents in surface-assisted laser desorption/ionization mass-spectrometry (SALDI-MS) for detecting pesticides in human blood serum. Specifically, ZnFe2O4, MnFe2O4, and CuFe2O4 NPs were synthesized using co-precipitation method and were characterized using different analytical techniques, including X-ray diffraction, UV-vis spectroscopy, X-ray photo-electron, respectively. Thus, the development of the high-efficiency SALDI technique will enable its use as an analytical tool in forensic investigation using minute volumes of sample and substrate and with minimum sample handling.Both Raman spectroscopy and laser-induced breakdown spectroscopy (LIBS) were cooperatively utilized to improve the geographical origin identification of raw sapphires from five different countries (Mozambique, Laos, Australia, Rwanda, and Congo). A hierarchical support vector machine (H-SVM) was used for multi-group identification. Initially, accuracy improved to 87.5% using merged Raman-LIBS data compared to those of using only Raman (82.8%) or LIBS (71.9%) information. This improvement was attributed to incorporating two complimentary spectroscopic datasets that provided molecular vibrational and elemental information. However, merging both spectroscopic datasets is may not be the best choice since it would make distinct and sample-descriptive information in one spectroscopic dataset less recognized for analysis by the inclusion of less characteristic information in another spectroscopic dataset; using only Raman or LIBS information at each discrimination stage would be more effective. When Raman information was utilized during the first three discrimination stages followed by LIBS data during the fourth (last) discrimination stage in H-SVM, the accuracy improved to 90.