Iranian Journal of Analytical Chemistry
Volume:11 Issue: 1, Winter and Spring 2024

Ethanol (EtOH) purification is a pivotal research pursuit, with liquid-liquid extraction emerging as a significant purification methodology. This study focuses on utilizing benzene solvent for EtOH purification and investigates the liquid-liquid equilibrium (LLE) within three-component systems comprising EtOH, water, and benzene. Thermodynamic modeling of EtOH-benzene-water systems at temperatures of 20 °C, 30 °C, 40 °C, and 55 °C was conducted. In this paper, the equations used for predicting mole fraction include Non-Random Two-Liquid (NRTL), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Multilayer Perceptron Artificial Neural Network (MLP-ANN). First, the equation parameters were optimized using the particle swarm optimization (PSO) algorithm to employ the NRTL equation Experimental data was used to train the MLP-ANN and ANFIS methods, and the same experimental datasets were used for all models. These models estimated integral components across both phases, revealing effective system control across all methodologies. However, the comparative analysis indicated the superior performance of the MLP-ANN and ANFIS methods over the NRTL model. The Root Mean Square Deviation (RMSD) errors for the NRTL, MLP-ANN, and ANFIS models were 0.0253, 0.0035, and 0.0017, respectively. These results indicate that despite the low prediction error of all three methods, the NRTL equation has the highest error, and the ANFIS method has the lowest mole fraction prediction error.

In chemical industries, precision in calculations and process simulations is crucial. One of the most influential parameters is the molar density of a fluid under various pressure and temperature conditions. Equations of state (EOS) are common among the methods for determining molar density. Usually, the error resulting from predicting molar density using EOS is generally high at high temperatures and pressures due to the increased intermolecular effects. Additionally, due to the form of EOS concerning volume or molar density, calculating molar volume at specified temperature and pressure requires suitable numerical methods for root-finding. This article aims to present an effective method for estimating the molar density of benzene using two crucial machine learning methods, namely Multi-Layer Perceptron-Artificial Neural Network(MLP-ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS). This study used 302 sets of experimental data to train these two methods. Additionally, another set of 60 experimental data was used to compare the errors of the methods. The Peng-Robinson (PR) equation was also employed in this article to evaluate the performance of machine learning methods better and calculate molar density. The results showed that the mean relative errors (MRE) for the MLP-ANN, ANFIS, and PR methods for the 362 data points are 0.838%, 1.791%, and 4.834%, respectively. The results demonstrated that using machine learning methods can reduce computational errors, with the error from predicting using the PR equation being almost five times that of MLP-ANN. In this article, the MLP-ANN method outperformed ANFIS due to its computational efficiency and lower error in predicting molar density.

Tobacco mosaic virus causes great economic damage to tobacco, pepper, cucumber and ornamental flowers all over the world. In the current work, the relationship between the structure and activity of novel series echinopsin derivatives containing acylhydrazone fragments as antiviral activity against tobacco mosaic virus (TMV) was studied using quantitative structure-activity relationship (QSAR) calculations and molecular docking analysis. Molecular docking analysis of echinopsin derivatives with tobacco mosaic virus (2OM3) protein was done using AutoDock software and descriptors such as binding energy, electroestatic energy and hydrogen bond energy were calculated. The negative values of the binding energy illustrated that the binding nature of these derivatives, as the ligand with the 2OM3 protein is strong. For QSAR model first, the dataset was divided into two groups of training and test sets. Then, descriptors were calculated using quantum mechanics, molecular docking and molecular descriptors. Then, modeling was done by multiple linear regression (MLR) method. It was found that of the lowest unoccupied molecular orbital (LUMO) and Gibbs free energy changes play a role in the model. Also, the descriptors of the total energy of van der Waals interactions, hydrogen bond and energy of vdW + H-bond + desolvation of the molecular docking descriptors have an effect in the regression model. This study can play an important role to design anti –TMV inhibitors.

In the present research, an effective adsorbent as titled multiwall carbon nanotube/ZnCo- Zeolite imidazole frameworks (MWC/ZIF) was prepared and used for removal of Pb(II) ion from effluent samples. After separating the adsorbent from the solution, the amount of Pb(II) ion in the solution was measured using an atomic absorption device. Based on this, various experimental parameters effective on lead removal including pH, ionic strength, time, temperature, and Pb(II) ion concentration were investigated. Various kinetic models were also studied to assess adsorption kinetics of Pb(II) ions onto surface of MWC/ZIF nanocomposite. With reference to the obtained findings, the produced nanocomposite was assumed as an effective adsorption approach for removal of Pb(II) ions from effluent samples.

Feature selection is crucial in Quantitative Structure-Activity Relationship (QSAR) studies, enhancing learning algorithms’ performance and reducing computational costs. This study evaluates the impact of eight variable selection methods on the classification of isoform-selective ligands for Bcl-2 and Bcl-xL targets using three machine learning techniques: Supervised Kohonen Network (SKN), Support Vector Machine (SVM), and Partial Least Squares Discriminant Analysis (PLS-DA). Classification models were assessed using confusion matrix parameters, 10-fold Venetian blind cross-validation, and test sets.The results show that PLS-DA and SVM have comparable classification capabilities, outperforming SKN. However, PLS-DA occasionally leaves some ligands unassigned, making SVM a more robust and efficient choice. Despite using different variable selection methods, no clear advantage was found for any specific method, with all achieving around 70% classification accuracy in validation and test series. This suggests that the choice of variable selection method does not consistently affect outcomes across all techniques.Ensuring the reliability of selected variables involves meticulous data quality assessments, literature review, and robust cross-validation. Eliminating redundant features is essential for accurate classification models, as many physicochemical properties may be irrelevant to target bioactivity. While no single method guarantees superior models, selecting important variables is vital for extracting relevant features. This study highlights the importance of careful variable selection in QSAR studies, emphasizing its role in reducing dimensionality and improving model interpretability. Ultimately, this enhances drug discovery efficiency by identifying safer and more effective compounds, reducing time and cost.

A novel colorimetric chemosensor for naked-eye detection and determination of Mn2+ and cysteine (Cys) based on indicator displacement assay (IDA) was designed using bromo pyrogallol red (BPR). The indicator exchange occurred between BPR and Cys by the addition of Cys to the Mn(BPR) complex, which is accomplished by an immediate visible color change from purple to magenta, in EtOH/HEPES buffer 10.0 mmol L-1, pH 9.3 (1:4 v/v). The proposed method exhibits a 0.02 μmol L−1 detection limit and good linearity in the range of 0.11–2.87 μmol L−1 for cysteine amino acid. Additionally, the absorption and color change obtained in this chemosensor operate as an “IMPLICATION” logic gate considering Mn2+ and Cys as inputs. Eventually, based on such a fast, reversible, and reproducible signal, a molecular-scale sequential memory unit was designed to display “keypad lock” behavior. The developed chemosensor presented satisfactory repeatability, good precision, and successful application for the selective determination of Cys in human biological fluids. Furthermore, the method's accuracy was evaluated by comparing the results obtained from the proposed method and those from the reference method.

Nd2Sn2O7 nanoceramic was synthesized using an eco-friendly method with SnCl4•5H2O and Nd(NO3)3•6H2O. Structural analysis confirmed the formation of Nd2Sn2O7 nanoceramic with a size of 20±8 nm. Nd2Sn2O7 was thoroughly characterized using SEM, XRD, TGA, EDX, and TEM techniques. Due to its high mechanical and long-term colloidal stability, large ionic character, and thermal stability, this system is considered an ideal nanocatalyst employing the host-guest approach. This green and environmentally friendly method was tested for the reduction of nitro-aromatic compounds using the synthesized Nd2Sn2O7 nanoceramic. The catalyst demonstrated easy and effective reusability after the reaction was completed under visible light irradiation.

This study presents the synthesis of Zn/La³⁺-based metal-organic frameworks (MOFs) using a co-precipitation assisted microwave method. Characterization through SEM and TEM revealed uniform nanoparticles around 80 nm. FT-IR spectroscopy confirmed the presence of key functional groups. Dynamic light scattering (DLS) showed highly uniform particle sizes. In vitro release studies of captopril from Zn/La³⁺/MOFs demonstrated a 41% release rate over 300 min, compared to 64% for pure captopril. Encapsulation within the MOF matrix ensured controlled and sustained drug release, with the first -order kinetic model fitting best. These Zn/La³⁺/MOFs show promise for enhanced and controlled drug delivery systems.

The photocatalytic degradation of organic dye residues offers a promising and eco-friendly solution to challenges that endanger living organisms. A highly efficient fibrous nanocatalyst was carefully fabricated, designed, and utilized to remove acid black 1, acid blue 92, acid brown 214, and acid violet 7 [Wastewater colors]. The findings indicated that the amount of Gd2ZnMnO6/ZnO quantum dots affects the degradation efficiency. Integrating quantum dots into the photocatalyst structure boosts light absorption, accelerates electron transfer rates, and enhances charge transfer efficiency. The catalyst's performance was assessed by considering various catalyst components for the removal of organic wastes. The study proposed rational procedures based on the interaction between Gd2ZnMnO6 and ZnO nanoparticles within the catalyst, which can be reused and recovered for at least 10 cycles without significant loss of reactivity.