Prediction of CO2 Solubility in Polymers by Radial Basis Function Artificial Neural Network Based on Chaotic Self-adaptive Particle Swarm Optimization and Fuzzy Clustering Method

To replace costly and time-consuming experimentation in laboratory, a novel solubility prediction model based on chaos theory, self-adaptive particle swarm optimization （PSO）, fuzzy c-means clustering method, and radial ba- sis function artificial neural network （RBF ANN） is proposed to predict CO2 solubility in polymers, hereafter called CSPSO-FC RBF ANN. The premature convergence problem is overcome by modifying the conventional PSO using chaos theory and self-adaptive inertia weight factor. Fuzzy c-means clustering method is used to tune the hidden centers and radial basis function spreads. The modified PSO algorithm is employed to optimize the RBF ANN connection weights. Then, the proposed CSPSO-FC RBF ANN is used to investigate solubility of CO2 in polystyrene （PS）, polypropylene （PP）, poly（butylene succinate） （PBS） and poly（butylene succinate-co-adipate） （PBSA）, respec- tively. Results indicate that CSPSO-FC RBF ANN is an effective method for gas solubility in polymers. In addition, compared with conventional RBF ANN and PSO ANN, CSPSO-FC RBF ANN shows better performance. The values of average relative deviation （ARD）, squared correlation coefficient （R2） and standard deviation （SD） are 0.1071, 0.9973 and 0.0108, respectively. Statistical data demonstrate that CSPSO-FC RBF ANN has excellent prediction capability and high-accuracy, and the correlation between prediction values and experimental data is good.     

Data-driven machine learning models for quick prediction of thermal stability properties of OLED materials

Organic light-emitting diode (OLED) materials have exhibited a wide range of applications. However, the further development and commercialization of OLEDs requires higher quality OLED materials, including materials with a high thermal stability. Thermal stability is associated with the glass transition temperature (T_g) and decomposition temperature (T_d), but experimental determinations of these two important properties generally involve a time-consuming and laborious process. Thus, the development of a quick and accurate prediction tool is highly desirable. Motivated by the challenge, we explored machine learning (ML) by constructing a new dataset with more than 1,000 samples collected from a wide range of literature, through which ensemble learning models were explored. Models trained with the LightGBM algorithm exhibited the best prediction performance, where the values of mean absolute error, root mean squared error, and R² were 17.15 K, 24.63 K, and 0.77 for T_g prediction and 24.91 K, 33.88 K, and 0.78 for T_d prediction. The prediction performance and the generalization of the ML models were further tested by two applications, which also exhibited satisfactory results. Experimental validation further demonstrated the reliability and the practical potential of the ML-based models. In order to extend the practical application of the ML-based models, an online prediction platform was constructed. This platform includes the optimal prediction models and all the thermal stability data under study, and it is freely available at http://www.oledtppxmpugroup.com. We expect that this platform will become a useful tool for experimental investigation of T_g and T_d, accelerating the design of OLED materials with desired properties.     

A comparative study of diesel analysis by FTIR, FTNIR and FT-Raman spectroscopy using PLS and artificial neural network analysis

Diesel properties determined by ASTM reference methods as cetane index, density, viscosity, distillation temperatures at 50% (T50) and 85% (T85) recovery, and the total sulfur content (%, w/w) were modeled by FTIR-ATR, FTNIR, and FT-Raman spectroscopy using partial last square regression (PLS) and artificial neural network (ANN) spectral analysis. In the PLS models, 45 diesel samples were used in the training group and the other 45 samples were used in the validation. In the ANN analysis a modular feedforward network was used. Sixty diesel samples were used in the neural network training and other 30 samples were used in the validation. Two different ATR configurations were compared in the FTIR, a conventional (ATR1) and an immersion (ATR2) cell. The ATR1 cell presented the best results, with smaller prediction errors (root mean square error of prediction, RMSEP). The comparison of the three PLS models (FTIR-ATR1, FTNIR, and FT-Raman) shows that reasonable values of R² and RMSEP were obtained by the FTIR-ATR1 and FTNIR models in the evaluation of density, viscosity, and T50. The PLS/FT-Raman models presented reasonable results only for the T50 property. None of the techniques was able to generate suitable PLS calibration models for the determination of sulfur content. The ANN/FT-Raman models presented the best performances, with all models presenting R²-values above 85% some of them with RMSEP values significantly smaller than those obtained with FTIR-ATR and FTNIR. The ANN/FT-Raman and ANN/FTIR-ATR1 models were able to estimate the total sulfur content of diesel with 0.01% (w/w) accuracy.     

Substantially enhancing the catalytic performance of bis(imino)pyridylcobaltous chloride pre‐catalysts adorned with benzhydryl and nitro groups for ethylene polymerization

Variations in the ligand structure of homogeneous late transition metal catalysts through judicious choice and location of substituent is the foremost strategy in improving their catalytic performance for ethylene polymerization. In this contribution, symmetrical and unsymmetrical bis(imino)pyridylcobaltous chloride complexes adorned with nitro and benzhydryl groups {2‐[1‐(2,6‐dibenzhydryl‐4‐nitrophenylimino)ethyl]‐6‐[1‐(alkylphenylimino)ethyl]pyridylcobaltous chloride (alkyl: R¹ = Me and R² = H, Co1 ; R¹ = Et and R² = H, Co2 ; R¹ = ⁱPr and R² = H, Co3 ; R¹ and R² = Me, Co4 ; R¹ = Et and R² = Me, Co5 ; R¹ = benzhydryl and R² = NO₂, Co6 )} have been prepared and applied as catalysts for ethylene polymerization. The molecular structure of Co1 and Co2 revealed the unequal steric protection of the cobalt center induced by bis(imino)pyridine chelate. In the presence of methylaluminoxane (MAO) or modified methylaluminoxane (MMAO) activators at different ethylene feeding rates (1 and 10 atm), catalysts Co1 – Co5 displayed high activities at 10 atm ethylene and produced strictly linear polyethylene (PE) with high molecular weight, Co2 /MMAO being the most highly active catalytic system showing the highest activity of 9.41 × 10⁶ g of PE (mol of Co)^?1 h^?1 which is three times higher than that of prototypal cobalt catalyst ( Co0 ) under identical conditions. Moreover, high melt temperature and unimodal molecular weight distribution are the characteristics of the resulting polyethylene.     

Experimental design,machine learning approaches for the optimization and modeling of caffeine adsorption

In the current research, the sorption of caffeine on fresh and calcined Cu–Al layered double hydroxide was comparatively studied based on adsorption parameters, adsorption kinetics, and adsorption isotherm. Response surface methodology (RSM), support vector machine (SVM) and artificial neural network (ANN), as data mining methods, were applied to develop models by considering various operating variables. Different characterization methods were exploited to conduct a comprehensive analysis of the characteristics of HDL in order to acquire a thorough understanding of its structural and functional features. The Langmuir model was employed to accurately describe the maximum monolayer adsorption capacity for calcined sample (q_max) of 152.99 mg/g mg/g with R² = 0.9977. The pseudo-second order model precisely described the adsorption phenomenon (R² = 0.999). The thermodynamic analysis also reveals a favorable and spontaneous process. The ANN model predicts adsorption efficiency result with R² = 0.989. The five-fold cross-validation was achieved to evaluate the validity of the SVM. The predication results revealed approximately 99.9% accuracy for test datasets and 99.63% accuracy for experiment data. Moreover, ANOVA analysis employing the central composite design-response surface methodology (CCD-RSM) indicated a good agreement between the quadratic equation predictions and the experimental data, which results in R² of 0.9868 and the highest removal percentages in optimized step were obtained for RSM (pH 5.05, mass of adsorbent 20 mg, time of 72 min, and caffeine concentrations of 22 mg/L). On the whole, the findings confirm that the proposed machine learning models provided reliable and robust computer methods for monitoring and simulating the adsorption of pollutants from aqueous solutions by Cu–Al–LDH.     

Iron(II) and cobalt(II) complexes bearing 8-(1-aryliminoethylidene) quinaldines: Synthesis, characterization and ethylene dimerization behavior

The series of bidentate N^N iron(II) and cobalt(II) complexes containing 8-(1-aryliminoethylidene) quinaldine derived ligands, 8-[2,6-(R¹)₂-4-R²-C₆H₂NC (Me)]-2-Me-C₁₀H₅N, were synthesized and characterized by elemental and spectroscopic techniques. The molecular structures of Co1 (R¹ = Me, R² = H), Co3 (R¹ = ⁱPr, R² = H) and Co4 (R¹ = R² = Me) were confirmed as the distorted tetrahedral by single crystal X-ray diffraction. On treatment with modified methylaluminoxane (MMAO), these complexes exhibited good catalytic activities of up to 5.71 × 10⁵ g mol⁻¹(Fe) h⁻¹ for the ethylene dimerization at 30 °C under 10 atm of ethylene, in which iron pre-catalysts produced butenes with a high selectivity for α-butene. The correlation between metal complexes, catalytic activities and the product formed were investigated under various reaction parameters.     

EuTZn (T=Pd, Pt, Au) with TiNiSi-type structure—Magnetic properties and Eu Mössbauer spectroscopy

The europium compounds EuTZn (T=Pd, Pt, Au) were synthesized from the elements in sealed tantalum tubes in an induction furnace. These intermetallics crystallize with the orthorhombic TiNiSi-type structure, space group Pnma. The structures were investigated by X-ray diffraction on powders and single crystals: a=732.3(2), b=448.5(2), c=787.7(2) pm, R₁/wR₂=0.0400/0.0594, 565 F² values for EuPdZn, a=727.8(3), b=443.7(1), c=781.7(3) pm, R₁/wR₂=0.0605/0.0866, 573 F² values for EuPtZn, and a=747.4(2), b=465.8(2), c=789.1(4) pm, R₁/wR₂=0.0351/0.0590, 658 F² values for EuAuZn, with 20 variables per refinement. Together the T and zinc atoms build up three-dimensional [TZn] networks with short T–Zn distances. The EuTZn compounds show Curie–Weiss behavior in the temperature range from 75 to 300 K with μ_eff=7.97(1), 7.70(1), and 7.94(1) μ_B/Eu atom and θ_P=18.6(1), 34.9(1), and 55.5(1) K for T=Pd, Pt, and Au, respectively, indicating divalent europium. Antiferromagntic ordering was detected at 15.1(3) K for EuPdZn and canted ferromagnetic ordering at 21.2(3) and 51.1(3) K for EuPtZn and EuAuZn. ¹⁵¹Eu Mössbauer spectroscopic measurements confirm the divalent nature of the europium atoms by isomer shift values ranging from −8.22(8) (EuPtZn) to −9.23(2) mm/s (EuAuZn). At 4.2 K full magnetic hyperfine field splitting is observed in all three compounds due to magnetic ordering of the europium magnetic moments.     

Polymers in 2D confinement: A nanoscale mechanism for thermo‐mechanical reinforcement

Acrylate‐clay nanocomposites, a 2D confined system, exhibited unusual increase of thermo‐mechanical properties. The nature of this reinforcement can be ascribed to chain dynamics modification and therefore investigated via dynamic mechanical analysis. Transmission electron microscopy and dynamic light scattering showed a strong nanoconfined regime, 2R_h ≫ d₀₀₁, where R_h is the polymer's hydrodynamic radius and d₀₀₁ is the clay gallery spacing. The geometrical constraints to polymer dynamics led to significant enhancement of the thermo‐mechanical properties. Adding only 1 wt% nanoclay, the glass transition temperature increased significantly, ΔT_g = T_g − T_g,bulk ~ 10°C, and the dynamic modulus E′ increased 10‐fold. Analysis of dynamic mechanical spectra showed an increase of relaxation time τ, ie, polymer dynamics retardation. Furthermore, the mechanical damping tan δ was strongly attenuated evidencing the reduction of viscous dissipation. The activation energy E_a of the α‐transition increased as the confined macromolecules needed to overcome higher energy barriers to achieve configurational rearrangements. The considerable increase of mechanical modulus cannot be explained by polymer composite models, rather it was associated to a “nano‐effect,” scaling with the degree of confinement as E/E_matrix ~ (2R_h/d₀₀₁)ⁿ. This study paves the road for further understanding of polymer dynamics under 2D confinement and the reinforcement mechanism of thermo‐mechanical properties.     

Effects of glow discharge plasma on Cu–Co–Al-based supported catalysts for higher alcohol synthesis

The novel plasma assisted Cu–Co/γ-Al₂O₃ catalysts were prepared by incipient impregnation method for CO hydrogenation to higher alcohols and characterized by means of scanning electron microscopy (SEM), N₂ adsorption, X-ray powder diffraction (XRD) and X-ray photoelectron spectroscopy (XPS) techniques. It was found that introduction of plasma significantly improved the specific surface area, dispersion of catalyst and the enrichment of active species on the surface of catalysts. Under the conditions of P = 5.0 MPa, GHSV = 6,000 h⁻¹, V(H₂)/V(CO) = 2, T = 573 K, the conversion of carbon monoxide over the plasma enhanced catalyst increased by 41.9% compared with that of the conventional sample, and the space time yield reached 337.1 g kg⁻¹ h⁻¹.     

Molecular description of pyrimidine-based inhibitors with activity against FAK combining 3D-QSAR analysis,molecular docking and molecular dynamics

Focal adhesion kinase (FAK) is a promising target for developing more effective anticancer drugs. To better understand the structure-activity relationships and mechanism of actions of FAK inhibitors, a molecular modeling study using 3D-QSAR, molecular docking, molecular dynamics simulations, and binding free energy analysis were conducted. Two types of satisfactory 3D-QSAR models were generated, comprising the CoMFA model (R²_cv = 0.528, R²_pred = 0.7557) and CoMSIA model (R²_cv = 0.757, R²_pred = 0.8362), for predicting the inhibitory activities of novel inhibitors. The derived contour maps indicate structural characteristics for substituents on the template. Molecular docking, molecular dynamic simulations and binding free energy calculations further reveal that the binding of inhibitors to FAK is mainly contributed from hydrophobic, electrostatic and hydrogen bonding interactions. In addition, some key residues (Arg14, Glu88, Cys90, Arg138, Asn139, Leu141, and Leu155) responsible for ligand-receptor binding are highlighted. All structural information obtained from 3D-QSAR models and molecular dynamics is consist with the available experimental activities. All the results will facilitate the optimization of this series of FAK inhibitors with higher inhibitory activities.