A simple, remote-sensed method of detection of traces of petroleum in soil combining artificial intelligence (AI) with mid-infrared (MIR) laser spectroscopy is presented. A portable MIR quantum cascade laser (QCL) was used as an excitation source, making the technique amenable to field applications. The MIR spectral region is more informative and useful than the near IR region for the detection of pollutants in soil. Remote sensing, coupled with a support vector machine (SVM) algorithm, was used to accurately identify the presence/absence of traces of petroleum in soil mixtures. Chemometrics tools such as principal component analysis (PCA), partial least square-discriminant analysis (PLS-DA), and SVM demonstrated the effectiveness of rapidly differentiating between different soil types and detecting the presence of petroleum traces in different soil matrices such as sea sand, red soil, and brown soil. Comparisons between results of PLS-DA and SVM were based on sensitivity, selectivity, and areas under receiver-operator curves (ROC). An innovative statistical analysis method of calculating limits of detection (LOD) and limits of decision (LD) from fits of the probability of detection was developed. Results for QCL/PLS-DA models achieved LOD and LD of 0.2% and 0.01% for petroleum/soil, respectively. The superior performance of QCL/SVM models improved these values to 0.04% and 0.003%, respectively, providing better identification probability of soils contaminated with petroleum.

This study investigated the deactivation and regeneration of hierarchical zeolites in vacuum gas oil conversion, aiming to reach the equilibrium state seen in fluidized bed catalytic cracking (FCC). The research utilized various characterization techniques to analyze the properties of zeolites before and after coking and regeneration. Zeolite Y-0.20-S was found to have the highest gasoline selectivity and quality, mirroring industrial yields, and displayed notable stability across deactivation/regeneration cycles. Higher mesopore concentration in zeolites led to increased coke selectivity and better resistance to deactivation. The study observed a dominance of aromatic coke with a higher degree of condensation in these zeolites. Despite coke deposition affecting acid and textural properties, the regeneration process effectively restored these characteristics, proving its efficiency. The zeolites with greater mesoporosity retained their fundamental properties responsible for activity and selectivity, highlighting the importance of selecting materials that provide high conversions and maintain stability and product selectivity over multiple cycles. The Y-0.20-S zeolite, in particular, was identified as a promising candidate for commercial catalyst development for gasoline production, contributing to the FCC process's energy efficiency.

The first examples of 3-D metal–organometallic networks (MOMNs) bearing 1,1′-ferrocenyldicarboxylate ligands have been obtained. These MOMNs exhibit a diamondoid-like network topology with 3-fold interpenetration described with point symbols for a net with loops, {6⁶}{6}2. Their electrochemical properties were evaluated by solution-state differential pulse voltammetry.

A tunable quantum cascade laser (QCL) spectrometer was used to develop methods for detecting and quantifying high explosives (HE) in soil based on multivariate analysis (MVA) and artificial intelligence (AI). For quantification, mixes of 2,4-dinitrotoluene (DNT) of concentrations from 0% to 20% w/w with soil samples were investigated. Three types of soils, bentonite, synthetic soil, and natural soil, were used. A partial least squares (PLS) regression model was generated for predicting DNT concentrations. To increase the selectivity, the model was trained and evaluated using additional analytes as interferences, including other HEs such as pentaerythritol tetranitrate (PETN), trinitrotoluene (TNT), cyclotrimethylenetrinitramine (RDX), and non-explosives such as benzoic acid and ibuprofen. For the detection experiments, mixes of different explosives with soils were used to implement two AI strategies. In the first strategy, the spectra of the samples were compared with spectra of soils stored in a database to identify the most similar soils based on QCL spectroscopy. Next, a preprocessing based on classical least squares (Pre-CLS) was applied to the spectra of soils selected from the database. The parameter obtained based on the sum of the weights of Pre-CLS was used to generate a simple binary discrimination model for distinguishing between contaminated and uncontaminated soils, achieving an accuracy of 0.877. In the second AI strategy, the same parameter was added to a principal component matrix obtained from spectral data of samples and used to generate multi-classification models based on different machine learning algorithms. A random forest model worked best with 0.996 accuracy and allowing to distinguish between soils contaminated with DNT, TNT, or RDX and uncontaminated soils.