Hydrodesulfurization of Dibenzothiophene: A Machine Learning Approach.

The hydrodesulfurization (HDS) process is widely used in the industry to eliminate sulfur compounds from fuels. However, removing dibenzothiophene (DBT) and its derivatives is a challenge. Here, the key aspects that affect the efficiency of catalysts in the HDS of DBT were investigated using machine learning (ML) algorithms.

Conceptual DFT, machine learning and molecular docking as tools for predicting LD toxicity of organothiophosphates.

Context: Several descriptors from conceptual density functional theory (cDFT) and the quantum theory of atoms in molecules (QTAIM) were utilized in Random Forest (RF), LASSO, Ridge, Elastic Net (EN), and Support Vector Machines (SVM) methods to predict the toxicity (LD) of sixty-two organothiophosphate compounds. The A-RF-G1 and A-RF-G2 models were obtained using the RF method, yielding statistically significant parameters with good performance, as indicated by R values for the training set (R) and R values for the test set (R), around 0.90.

A DFT Study of the Reaction of Acrylamide with L-Cysteine and L-Glutathione.

Thermal processing of certain foods implies the formation of acrylamide, which has been proven to provoke adverse effects on human health. Thus, several strategies to mitigate it have been developed. One of them could be the application of organosulfur compounds obtained from natural sources to react with the acrylamide, forming non-toxic adducts.

QSTR Modeling to Find Relevant DFT Descriptors Related to the Toxicity of Carbamates.

Compounds containing carbamate moieties and their derivatives can generate serious public health threats and environmental problems due their high potential toxicity. In this study, a quantitative structure-toxicity relationship (QSTR) model has been developed by using one hundred seventy-eight carbamate derivatives whose toxicities in rats (oral administration) have been evaluated. The QSRT model was rigorously validated by using either tested or untested compounds falling within the applicability domain of the model.

New QSPR model for prediction of corrosion inhibition using conceptual density functional theory.

The relationship between structure and corrosion inhibition of a series of twenty-eight quinoline and pyridine derivatives has been established through the investigation of quantum descriptors calculated with PBE/6-311 +  + G** method. A quantitative structure-property relationship (QSPR) model was obtained by examining these descriptors using a genetic algorithm approximation method based on a multiple linear regression analysis. The results indicate that the efficiency of corrosion inhibitors is strongly associated with hardness (η), minimal electrostatic potential (ESP), and volume (V) descriptors.

The chemical reactivity and QSPR of organic compounds applied to dye-sensitized solar cells using DFT.

The structural and electronic properties were calculated for seventy organic compounds used as dye sensitizers in solar cells, applying the B3LYP exchange-correlation energy functional with the 6-311G∗∗ basis set. Moreover, the present study proposes two new quantitative structure-property relationship (QSPR) models that enable the prediction of the power conversion efficiency (PCE) and maximum absorption wavelength (λ) of these systems, the two QSPR models were validated using the coefficient of determination (R) of 0.62 for both models with the leave-one-out cross-validation correlation coefficient (Q) of 0.

Mathematical Models Generated for the Prediction of Corrosion Inhibition Using Different Theoretical Chemistry Simulations.

The use of corrosion inhibitors is an important method to retard the process of metallic attack by corrosion. The construction of mathematical models from theoretical-computational and experimental data obtained for different molecules is one of the most attractive alternatives in the analysis of corrosion prevention, whose objective is to define those molecular characteristics that are common in high-performance corrosion inhibitors. This review includes data of corrosion inhibitors evaluated in different media, the most commonly studied molecular descriptors, and some examples of mathematical models generated by different researchers.

Spectroscopic studies and molecular modelling of the aflatoxin M1-bovine α-lactalbumin complex formation.

Since the high incidence of aflatoxin M1 (AFM1) in milk and dairy products poses a serious risk to human health, this work aimed to investigate the complex formation between bovine α-lactalbumin (α-La) and AFM1 using different spectroscopic methods coupled with molecular docking studies. Fluorescence spectroscopy measurements demonstrated the AFM1 addition considerably reduced the α-La fluorescence intensity through a static quenching mechanism. The results indicated on the endothermic character of the reaction, and the hydrophobic interaction played a major role in the binding between AFM1 and α-La.

A theoretical and experimental approach to evaluate zein-calcium interaction in nixtamalization process.

The possible interactions between α-zein and Ca in nixtamalization process were analyzed from a multidisciplinary approach, considering the effect of these interactions on the thermal properties of the nixtamalized flour. SDS-PAGE under reducing and non-reducing conditions did not reveal differences between patterns of zeins from nixtamalized and control samples. However, analysis from affinity capillary electrophoresis indicated an increment in protein volume when calcium is added to zein extracted from nixtamalized flour.