AB CROSSLINKED POLYURETHANES THROUGH IONIC CROSSLINKING: INFLUENCE OF CROSSLINKING NETWORKS ON PHYSICO CHEMICAL PROPERTIES

Isocyanate-terminated prepolymers were synthesized using poly(tetramethylene oxide)glycol of molecular weight 1000 (PTMG₁₀₀₀) with tolylene-2,4-diisocyanate (TDI). The prepolymers were chain extended with N-methyldiethanolamine (N-MDEA) to form polyurethanes containing tertiary nitrogen. These polyurethanes were crosslinked with bromine terminated polyurethane, poly(urethane-imide), and poly(urethane-siloxane) through the formation of cationomers at tertiary nitrogen sites across the backbone polyurethanes.

The crosslinked cationomeric polyurethanes were characterized by Fourier Transform Infrared Spectroscopy (FTIR), Thermogravimetric Analysis (TGA), mechanical analyses, (static and dynamic), and static contact angles measurements. FTIR spectral studies confirms the formation of bromine terminated poly(urethane-imide) and poly(urethane-siloxane), as well as quaternization of the tertiary nitrogen which leads to crosslinking. A comparison of thermal stabilities of crosslinked polymers with respect to the chemical nature of bromine terminated prepolymers (BTP) indicates improved thermal stability for poly(urethane-imide) based ABCP. Stress-strain analysis shows high elongation values for poly(urethane-siloxane) and poly(urethane-imide) based ABCPs. Dynamic mechanical analysis reveals better damping for poly(urethane- siloxane) based AB crosslinked polymers.     

An in silico expert system for the identification of eye irritants

This report describes development of an in silico, expert rule-based method for the classification of chemicals into irritants or non-irritants to eye, as defined by the Draize test. This method was developed to screen data-poor cosmetic ingredient chemicals for eye irritancy potential, which is based upon exclusion rules of five physicochemical properties – molecular weight (MW), hydrophobicity (log P), number of hydrogen bond donors (HBD), number of hydrogen bond acceptors (HBA) and polarizability (Pol). These rules were developed using the ADMET Predictor software and a dataset of 917 eye irritant chemicals. The dataset was divided into 826 (90%) chemicals used for training set and 91 (10%) chemicals used for external validation set (every 10th chemical sorted by molecular weight). The sensitivity of these rules for the training and validation sets was 72.3% and 71.4%, respectively. These rules were also validated for their specificity using an external validation set of 2011 non-irritant chemicals to the eye. The specificity for this validation set was revealed as 77.3%. This method facilitates rapid screening and prioritization of data poor chemicals that are unlikely to be tested for eye irritancy in the Draize test.     

Calorimetric Estimation Employing the Unscented Kalman Filter for a Batch Emulsion Polymerization Reactor

Reaction calorimetry is a very useful tool to monitor exothermic polymerization reactions as it is based on the estimation of the heat generated by the reaction. The objective of this work is to analyze the performance of an unscented Kalman filter (UKF) for online monitoring of batch vinyl acetate emulsion polymerization reactions. Reactions are performed in isoperibolic and isothermal conditions. The UKF is compared to an extended Kalman filter that has a very poor performance. The results show that the UKF is able to provide good estimates for the conversion, for the reactor and jacket temperatures, for the overall heat transfer coefficient between the reaction medium and the jacket, and for the heat loss from the jacket to the surroundings.

     

END样本最近邻密度估计的强相合速度

本文研究了扩展负相依(END)样本最近邻密度估计的强相合性问题.利用END序列的Bernstein型不等式和截尾的方法,获得了END样本最近邻密度估计的强相合速度,推广了NA样本和ND样本最近邻密度估计的相应结果.     

Extended X‐ray absorption fine structure and multiple‐scattering simulation of nickel dithiolene complexes Ni[S2C2(CF3)2]2n (n = −2, −1, 0) and an olefin adduct Ni[S2C2(CF3)2]2(1‐hexene)

A series of Ni dithiolene complexes Ni[S₂C₂(CF₃)]₂ⁿ (n = ?2, ?1, 0) ( 1 , 2 , 3 ) and a 1‐hexene adduct Ni[S₂C₂(CF₃)₂]₂(C₆H₁₂) ( 4 ) have been examined by Ni K‐edge X‐ray absorption near‐edge structure (XANES) and extended X‐ray absorption fine‐structure (EXAFS) spectroscopies. Ni XANES for 1 – 3 reveals clear pre‐edge features and approximately +0.7 eV shift in the Ni K‐edge position for `one‐electron' oxidation. EXAFS simulation shows that the Ni—S bond distances for 1 , 2 and 3 (2.11–2.16 Å) are within the typical values for square planar complexes and decrease by ～0.022 Å for each `one‐electron' oxidation. The changes in Ni K‐edge energy positions and Ni—S distances are consistent with the `non‐innocent' character of the dithiolene ligand. The Ni—C interactions at ～3.0 Å are analyzed and the multiple‐scattering parameters are also determined, leading to a better simulation for the overall EXAFS spectra. The 1‐hexene adduct 4 presents no pre‐edge feature, and its Ni K‐edge position shifts by ?0.8 eV in comparison with its starting dithiolene complex 3 . Consistently, EXAFS also showed that the Ni—S distances in 4 elongate by ～0.046 Å in comparison with 3 . The evidence confirms that the neutral complex is `reduced' upon addition of olefin, presumably by olefin donating the π‐electron density to the LUMO of 3 as suggested by UV/visible spectroscopy in the literature.     

面向数据挖掘的静态电源综合故障诊断研究

静态电源是机场桥载设备最重要的组成部件之一,应用非常广泛。然而,其产生的频发故障会造成设备利用率低、修复率时长和经济损失等问题,在基于桥载设备的安监系统上,提出了静态电源综合故障诊断方法,通过数据挖掘软件,建立了静态电源故障诊断预测模型。通过在线数据库测试结果表明得到了综合故障诊断方法在预测静态电源故障上显现的特点,得到了静态电源的未来状态,实现了对静态电源的实时故障进行预测,进而为解决故障提供方向和目标,最终达到降低经济损失最大化的目的。     

Selective etching of ZnTe in HF:H2O2:H2O solution: Interpretation of extended defect‐related etch figures

HF:H₂O₂:H₂O solution (40%wt.HF: 30wt.%H₂O₂: H₂O, 3:2:1 by volume) was used to reveal extended defects (line, face and volume defects) in bulk ZnTe crystals grown from Te solution. The etch patterns were analyzed based on their size, shape and distribution. The etch figures, both in the shape of pits and hillocks with high resolution, show forms controlled by the symmetries of the respective faces were produced. Two different sizes of pits were observed, the larger‐size pits correspond to dislocations penetrating the surface, however, the smaller‐size texture pits are produced on the defect‐free region, which serve as standard pits on respect faces. The face defects, such as grain boundaries, sub‐grain boundaries, dislocation walls, twins and stacking faults, can be all displayed clearly. Another essential feature of the etchant is that, it can effectively dissolve Te‐rich phase (Te inclusion/precipitates), which makes it promising to reveal the shape of this volume defect.     

A mathematical discussion of Pons Viver's implementation of Löwdin's spin projection operator

Recently, the molecular electronic structure theories for efficiently treating static (or strong) correlation in a black-box manner have attracted much attention. In these theories, a spin projection operator is used to recover the spin symmetry of a broken-symmetry Slater determinant. Very recently, Pons Viver proposed the practical and exact implementation of Löwdin's spin projection operator (Int. J. Quantum Chem. 2019, 119, e25770). In the present study, we attempt to supply mathematical proofs to Pons Viver's proposals and show a condition for establishing Pons Viver's implementation. Moreover, we explicitly derive the (spin projected) extended Hartree-Fock (EHF) equations on the basis of the model of common orbitals (ie, closed-shell orbitals used in the restricted open-shell Hartree-Fock (ROHF) method), which was combined by Pons Viver with the EHF method.     

Predicting the Standard Enthalpy (ΔHo f) and Entropy (So) of Alkanes by Artificial Neural Networks

Abstract

Artificial Neural Networks (ANNs) with Extended Delta-Bar-Delta (EDBD) back propagation learning algorithm have been developed to predict the standard enthalpy and entropy of 87 acyclic alkanes. Molecular weight, boiling point and density of the compounds were used as input parameters. The network's architecture and parameters were optimized to give maximum performances. The best network was a 3-6-2 ANN, and the optimum learning epoch was about 1320. The results show that the maximum relative errors of enthalpy and entropy are less than 3%. They reveal that the performances of ANNs for predicting the enthalpy and entropy of alkanes are satisfying.