Computational modeling of Hg/Ni ions separation via MOF/LDH nanocomposite: Machine learning based modeling

Nowadays, sustainable supplement of water has recently been identified as a vital necessity due to the existence of limited drinkable water sources. To do this, various techniques are being developed to remove various types of pollutants from water/wastewater sources. Adsorption of common water pollutants using nanocomposite materials has been of great popularity in recent years due to its high efficiency. This paper aims to develop various models based on machine learning approach to study their efficiency on predicting the experimentally measured results of Hg/Ni ions removal from water sources. To do this, this study attempts regression on a small data set using two parameters as inputs and two parameters as outputs. In this dataset, the inputs are Ion and C0, and the outputs are Ce and Qe. AdaBoost (Adaptive Boosting), a well-known ensemble method, was applied on top of three different models, including Decision Tree Regression (DT), Gaussian Process Regression (GPR), and Linear Regression (LR). After fine-tuning their hyper-parameters, the optimized model was evaluated through various metrics. For example, the R² for ADA + GPR model has a score of 0.998 for Ce and 0.999 for Qe as the best model among these three models. This model in RMSE is the best and illustrates 0.1512 and 1.490 for Ce and Qe as error. Eventually, ADA + GPR has been selected as the optimized model with optimized dataset: (Ion = Ni, C0 = 250, Ce = 206.0). But for Qe, different amounts are illustrated: (Ion = Hg, C0 = 106.7, Ce = 577.35)     

Effect of selected dopants on conductivity and moisture stability of Li3PS4 sulfide solid electrolyte: a first-principles study

The first principle computational screening was performed to investigate the effect of selected dopants for Li₃PS₄ sulfide solid electrolyte on its ionic conductivity and stability toward moisture. The results suggest that substitution P⁵⁺ using isovalent cations whose electronegativity (EN) value is closer to the value of S has more significant effects on the ionic conductivity, whereby W⁵⁺ and Sb⁵⁺ can improve most. Similarly, aliovalent cation substitutions with compensating changes in the lithium-ion concentration, particularly those with a lower oxidation state and higher EN, such as Cu²⁺, effectively enhance the lithium-ion conductivity in this structure. For cation dopants, it is found that ionic conductivity improvement of Li₃PS₄ is the synergetic effect of EN and oxidation number of the dopant as well as the material's lattice parameter change. Oxides of the considered cation dopants can also improve the ionic conductivity of the material but have much lower lithium-ion conductivity than the cases of cation dopants. However, the metal oxide dopants, particularly those derived from soft Lewis' acid cations, show a marginal improvement in moisture stability of the Li₃PS₄ electrolyte. The effect of halides and metal halide dopants on the lithium-ion conductivity and moisture stability of Li₃PS₄ electrolyte are also studied. It is found that metal halides are more effective than any other dopants in improving the ionic conductivity of Li₃PS₄.     

DFT study on the chemical sensitivity of C3N nanotubes toward acetone

Potential application of single-walled C₃N nanotubes was investigated as chemical sensors for acetone molecules based on the density functional theory calculations. It was found that the pristine nanotube weakly adsorbs an acetone molecule with the adsorption energy of − 9.7 kcal/mol, and its electronic properties are not sensitive to this molecule. By replacing a C atom with a Si atom, the nanotube becomes a p-type semiconductor. The adsorption energy of the acetone molecule on the Si-doped nanotube becomes much more negative (E_ad=−67.4 kcal/mol). The adsorption process leads to a sizable increase in the resistance of the Si-doped tube, thereby, it can show the presence of acetone molecule, creating an electronic signal. Also, the sensitivity of these devices can be controlled by the doping level of Si atoms. By increasing the number of dopant atoms from 1 to 4, the sensitivity is gradually increased.     

A regularised boundary element formulation for contactless SAR evaluations within homogeneous and inhomogeneous head phantoms

This work presents a Boundary Element Method (BEM) formulation for contactless electromagnetic field assessments. The new scheme is based on a regularised BEM approach that requires the use of electric measurements only. The regularisation is obtained by leveraging on an extension of Calderón techniques to rectangular systems leading to well-conditioned problems independent of the discretisation density. This enables the use of highly discretized Huygens surfaces that can be consequently placed very near to the radiating source. In addition, the new regularised scheme is hybridised with both surfacic homogeneous and volumetric inhomogeneous forward BEM solvers accelerated with fast matrix–vector multiplication schemes. This allows for rapid and effective dosimetric assessments and permits the use of inhomogeneous and realistic head phantoms. Numerical results corroborate the theory and confirms the practical effectiveness of all newly proposed formulations.     

Accelerated Nuclear Magnetic Resonance Spectroscopy with Deep Learning

Nuclear magnetic resonance (NMR) spectroscopy serves as an indispensable tool in chemistry and biology but often suffers from long experimental times. We present a proof‐of‐concept of the application of deep learning and neural networks for high‐quality, reliable, and very fast NMR spectra reconstruction from limited experimental data. We show that the neural network training can be achieved using solely synthetic NMR signals, which lifts the prohibiting demand for a large volume of realistic training data usually required for a deep learning approach.     

Artificial intelligence–based models for the qualitative and quantitative prediction of a phytochemical compound using HPLC method

Isoquercitrin is a flavonoid chemical compound that can be extracted from different plant species such as Mangifera indica (mango), Rheum nobile , Annona squamosal , Camellia sinensis (tea), and coriander ( Coriandrum sativum L.). It possesses various biological activities such as the prevention of thromboembolism and has anticancer, antiinflammatory, and antifatigue activities. Therefore, there is a critical need to elucidate and predict the qualitative and quantitative properties of this phytochemical compound using the high performance liquid chromatography (HPLC) technique. In this paper, three different nonlinear models including artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM),in addition to a classical linear model [multilinear regression analysis (MLR)], were used for the prediction of the retention time (tR) and peak area (PA) for isoquercitrin using HPLC. The simulation uses concentration of the standard, composition of the mobile phases (MP-A and MP-B), and pH as the corresponding input variables. The performance efficiency of the models was evaluated using relative mean square error (RMSE), mean square error (MSE), determination coefficient (DC), and correlation coefficient (CC). The obtained results demonstrated that all four models are capable of predicting the qualitative and quantitative properties of the bioactive compound. A predictive comparison of the models showed that M3 had the highest prediction accuracy among the three models. Further evaluation of the results showed that ANFIS–M3 outperformed the other models and serves as the best model for the prediction of PA. On the other hand, ANN–M3proved its merit and emerged as the best model for tR simulation. The overall predictive accuracy of the best models showed them to be reliable tools for both qualitative and quantitative determination.     

An Autonomous Chemical Robot Discovers the Rules of Inorganic Coordination Chemistry without Prior Knowledge

We present a chemical discovery robot for the efficient and reliable discovery of supramolecular architectures through the exploration of a huge reaction space exceeding ten billion combinations. The system was designed to search for areas of reactivity found through autonomous selection of the reagent types, amounts, and reaction conditions aiming for combinations that are reactive. The process consists of two parts where reagents are mixed together, choosing from one type of aldehyde, one amine and one azide (from a possible family of two amines, two aldehydes and four azides) with different volumes, ratios, reaction times, and temperatures, whereby the reagents are passed through a copper coil reactor. Next, either cobalt or iron is added, again from a large number of possible quantities. The reactivity was determined by evaluating differences in pH, UV‐Vis, and mass spectra before and after the search was started. The algorithm was focused on the exploration of interesting regions, as defined by the outputs from the sensors, and this led to the discovery of a range of 1‐benzyl‐(1,2,3‐triazol‐4‐yl)‐N‐alkyl‐(2‐pyridinemethanimine) ligands and new complexes: [Fe(L¹)₂](ClO₄)₂ ( 1 ); [Fe(L²)₂](ClO₄)₂ ( 2 ); [Co₂(L³)₂](ClO₄)₄ ( 3 ); [Fe₂(L³)₂](ClO₄)₄ ( 4 ), which were crystallised and their structure confirmed by single‐crystal X‐ray diffraction determination, as well as a range of new supramolecular clusters discovered in solution using high‐resolution mass spectrometry.     

Investigation of dense slurry suspensions with coaxial mixers: Influences of design variables through tomography and mathematical modelling

The coaxial mixers enhance the suspension of concentrated slurries in an agitated reactor. In this research work, the complex slurry suspension and dissemination behavior in a coaxial slurry mixing system (comprised of a close clearance anchor rotating with a low speed and an inner axial impeller rotating with a high speed) was analyzed employing ERT (electrical resistance tomography, a non-intrusive flow visualization technique), and computational fluid dynamics (CFD). The numerical models were validated by comparing the axial solid concentration profiles generated using the ERT data and the CFD simulation results. The influences of various important parameters such as the diameter of the inner axial impeller, the inner impeller type, and the inner impeller spacing on the hydrodynamic characteristics of the slurry suspensions in a coaxial mixing vessel were thoroughly analyzed. The radial and axial velocity profiles of solid particles were generated using the validated mathematical models. The assessment of energy loss due to the solid–solid collisions, the particle–fluid frictions, and the particle–vessel wall collisions was conducted. The evaluation of optimum inner impeller clearance and inner impeller diameter is essential to attain a high degree of solids suspension and dissemination in a coaxial slurry mixing system.     

Drug Design—Past,Present, Future

Drug design is a complex pharmaceutical science with a long history. Many achievements have been made in the field of drug design since the end of 19th century, when Emil Fisher suggested that the drug–receptor interaction resembles the key and lock interplay. Gradually, drug design has been transformed into a coherent and well-organized science with a solid theoretical background and practical applications. Now, drug design is the most advanced approach for drug discovery. It utilizes the innovations in science and technology and includes them in its wide-ranging arsenal of methods and tools in order to achieve the main goal: discovery of effective, specific, non-toxic, safe and well-tolerated drugs. Drug design is one of the most intensively developing modern sciences and its progress is accelerated by the implication of artificial intelligence. The present review aims to capture some of the most important milestones in the development of drug design, to outline some of the most used current methods and to sketch the future perspective according to the author’s point of view. Without pretending to cover fully the wide range of drug design topics, the review introduces the reader to the content of Molecules’ Special Issue “Drug Design—Science and Practice”.