Computational and experimental assessment of efficient dye adsorption method from aqueous effluents by halloysite and palygorskite clay minerals.

The removal of dyes from effluents of textile industries represents a technological challenge, due to their significant environmental impact. The application of halloysite (Hal) and palygorskite (Pal) clay minerals as adsorbents for the removal of Congo red (CR) and methylene blue (MB) was evaluated in this work. The materials were applied both in natural and acid-treated forms, and characterized by XRD, XPS, SEM-EDS, FTIR, and N2 adsorption-desorption isotherm techniques to identify their properties and main active sites. The adsorbents showed potential to remove CR (> 98%) and MB (> 85%) within 180 min, using 0.3 g adsorbent and initial dye concentration of 250 mg L-1. Semi-empirical quantum mechanical calculations (SQM) confirmed the interaction mechanism between dyes and the adsorbents via chemisorption (- 69.0 kcal mol-1 < Eads < - 28.8 kcal mol-1), which was further observed experimentally due to the high fit of adsorption data to pseudo-second order kinetic model (R2 > 0.99) and Langmuir isotherm (R2 > 0.98). The use of Pal and Hal to remove dyes was proven to be economically and environmentally viable for industrial application.

Multivariate strategy for identifying and quantifying jet fuel contaminants by MCR-ALS/PLS models coupled to combined MIR/NIR spectra.

The investigation and control of jet fuel contamination for private aircrafts has gained attention due to the softer monitoring in comparison to commercial aviation. The possible contamination with kerosene solvent (KS) makes this investigation more challenging, since it has physicochemical similarities with jet fuel. To help solve this problem, a chemometric methodology was applied in this research combining multivariate curve resolution with alternating least squares (MCR-ALS) and partial least squares (PLS) models coupled to near- and mid-infrared spectroscopies (MIR/NIR) in order to detect and quantify KS in blends with JET-A1 using 23 samples (5-60% v/v). Additionally, 98 samples were stored for 60 days, and principal component analysis, genetic algorithm, and successive projections algorithm were coupled to linear discriminant analysis (PCA-LDA, GA-LDA, and SPA-LDA) in order to classify the blends according to the bands assigned to oxidation products, such as phenols and carboxylic acids. GA-LDA and SPA-LDA models were accurate and reached 100% sensitivity and specificity. Physicochemical analysis was not able to detect the presence of KS in contaminated jet fuel samples, even in high concentrations. The use of MIR-NIR combined spectra improved the quantification results, thus decreasing the experimental error from 5.22% (using only NIR) to 1.64%. PLS regression quantified the content of KS with high accuracy (RMSEP < 1.64%, R2 > 0.995). The MCR-ALS model stood out for recovering the spectral profile of kerosene solvent by segregating it from jet fuel spectra. The development of models using chemometric tools contributed to a fast, low-cost, and efficient process for quality control that can be applied in the fuel industry.

Advances in chemometric control of commercial diesel adulteration by kerosene using IR spectroscopy.

Adulteration is a recurrent issue found in fuel screening. Commercial diesel contamination by kerosene is highly difficult to be detected via physicochemical methods applied in market. Although the contamination may affect diesel quality and storage stability, there is a lack of efficient methodologies for this evaluation. This paper assessed the use of IR spectroscopies (MIR and NIR) coupled with partial least squares (PLS) regression, support vector machine regression (SVR), and multivariate curve resolution with alternating least squares (MCR-ALS) calibration models for quantifying and identifying the presence of kerosene adulterant in commercial diesel. Moreover, principal component analysis (PCA), successive projections algorithm (SPA), and genetic algorithm (GA) tools coupled to linear discriminant analysis were used to observe the degradation behavior of 60 samples of pure and kerosene-added diesel fuel in different concentrations over 60 days of storage. Physicochemical properties of commercial diesel with 15% kerosene remained within conformity with Brazilian screening specifications; in addition, specified tests were not able to identify changes in the blends' performance over time. By using multivariate classification, the samples of pure and contaminated fuel were accurately classified by aging level into two well-defined groups, and some spectral features related to fuel degradation products were detected. PLS and SVR were accurate to quantify kerosene in the 2.5-40% (v/v) range, reaching RMSEC < 2.59% and RMSEP < 5.56%, with high correlation between real and predicted concentrations. MCR-ALS with correlation constraint was able to identify and recover the spectral profile of commercial diesel and kerosene adulterant from the IR spectra of contaminated blends.