Catalytic Mechanisms of Transfer Hydrogenation of Azobenzene with Ammonia Borane by Pincer Bismuth Complex: Crucial Role of C=N Functional Group on the Pincer Ligand

Transfer hydrogenation of azobenzene with ammonia borane mediated by pincer bismuth complex 1 was systematically investigated through density functional theory calculations. An unusual metal-ligand cooperation mechanism was disclosed, in which the saturation/regeneration of the C=N functional group on the pincer ligand plays an essential role. The reaction is initiated by the hydrogenation of the C=N bond (saturation) with ammonia borane to afford 3^CN , which is the rate-determining step with Gibbs energy barrier (ΔG^≠) and Gibbs reaction energy (ΔG) of 25.6 and −7.3 kcal/mol, respectively. 3^CN is then converted to a Bi−H intermediate through a water-bridged pathway, which is followed up with the transfer hydrogenation of azobenzene to produce the final product N,N′-diphenylhydrazine and regenerate the catalyst. Finally, the catalyst could be improved by substituting the phenyl group for the tert-butyl group on the pincer ligand, where the ΔG^≠ value (rate-determining step) decreases to 24.0 kcal/mol.     

A quantum extension to inspection game

Quantum game theory is a new interdisciplinary field between game theory and system engineering research. In this paper, we extend the classical inspection game into a quantum game version by quantizing the strategy space and importing entanglement between players. Our results show that the quantum inspection game has various Nash equilibria depending on the initial quantum state of the game. It is also shown that quantization can respectively help each player to increase his own payoff, yet fails to bring Pareto improvement for the collective payoff in the quantum inspection game.     

New insight into selective catalytic reduction of nitrogen oxides by ammonia over H-form zeolites: a theoretical study

Density functional theory calculations were carried out to investigate the reaction mechanism of selective catalytic reduction of nitrogen oxides by ammonia in the presence of oxygen at the Br?nsted acid sites of H-form zeolites. The Br?nsted acid site of H-form zeolites was modeled by an aluminosilicate cluster containing five tetrahedral (Al, Si) atoms. A low-activation-energy pathway for the catalytic reduction of NO was proposed. It consists of two successive stages: first NH(2)NO is formed in gas phase, and then is decomposed into N(2) and H(2)O over H-form zeolites. In the first stage, the formation of NH(2)NO may occur via two routes: (1) NO is directly oxidized by O(2) to NO(2), and then NO(2) combines with NO to form N(2)O(3), which reacts with NH(3) to produce NH(2)NO; (2) when NO(2) exceeds NO in the content, NO(2) associates with itself to form N(2)O(4), and then N(2)O(4) reacts with NH(3) to produce NH(2)NO. The second stage was suggested to proceed with low activation energy via a series of synergic proton transfer steps catalyzed by H-form zeolites. The rate-determining step for the whole reduction of NO(x) is identified as the oxidation of NO to NO(2) with an activation barrier of 15.6 kcal mol(-1). This mechanism was found to account for many known experimental facts related to selective catalytic reduction of nitrogen oxides by ammonia over H-form zeolites.     

Synthesis of Calix[6]arene-Porphyrin Receptors and Study on their Catalytic Performance in Epoxidation of Styrene

The artificial receptors with multiple recognition site had been synthesized by linking two unsymmetrical monofunctionalized porphyrin to p-tert-butyl calix[6]-arene via two flexible chain, followed by inserting metal ion into the porphyrin species.The structures (see Fig 1) of these calix[6]arene-diporphyrins and their metal complexes were confirmed by UV-vis spectra, IR spectra, mass spectra, ¹NMR spectra(600 MHz) and/or ¹³C NMR. Their conformations were investigated preliminarily by UV-vis spectra and computer simulation. In constrast with the correspounding porphyrin monomer, the soret bond of calix[6]arene-diporphyrins in UV-vis spectra showed hypsochromic shift. This phenomenon suggested that two porphyrin rings maintain a face to face parallel conformation, as confirmed by computer simulation that showed the conformation energy is the lowest as two porphyrin rings hold such a conformation. Besides, simplicity of the ¹NMR spectra indicates that the conformation of calix[6]arene-diporphyrin possesses high symmetry. Therefore,the calix[6]arene must be cone conformation.     

以红外3100～2800cm-1谱分析聚丁二烯

研究了红外3100～2800cm^-1谱区的聚丁二烯图谱,它是聚丁二烯的顺-1,4,反-1,4和1,2微观结构信息叠加谱区。用因素分析技术对聚丁二烯的微观结构进行了定量分析,与传统的分析方法和其它谱区的因素分析进行比较,结果显示3100～2800cm^-1港区的因素分析为现用方法中最佳。     

Computational and data driven molecular material design assisted by low scaling quantum mechanics calculations and machine learning

Electronic structure methods based on quantum mechanics (QM) are widely employed in the computational predictions of the molecular properties and optoelectronic properties of molecular materials. The computational costs of these QM methods, ranging from density functional theory (DFT) or time-dependent DFT (TDDFT) to wave-function theory (WFT), usually increase sharply with the system size, causing the curse of dimensionality and hindering the QM calculations for large sized systems such as long polymer oligomers and complex molecular aggregates. In such cases, in recent years low scaling QM methods and machine learning (ML) techniques have been adopted to reduce the computational costs and thus assist computational and data driven molecular material design. In this review, we illustrated low scaling ground-state and excited-state QM approaches and their applications to long oligomers, self-assembled supramolecular complexes, stimuli-responsive materials, mechanically interlocked molecules, and excited state processes in molecular aggregates. Variable electrostatic parameters were also introduced in the modified force fields with the polarization model. On the basis of QM computational or experimental datasets, several ML algorithms, including explainable models, deep learning, and on-line learning methods, have been employed to predict the molecular energies, forces, electronic structure properties, and optical or electrical properties of materials. It can be conceived that low scaling algorithms with periodic boundary conditions are expected to be further applicable to functional materials, perhaps in combination with machine learning to fast predict the lattice energy, crystal structures, and spectroscopic properties of periodic functional materials.

Low scaling quantum mechanics calculations and machine learning can be employed to efficiently predict the molecular energies, forces, and optical and electrical properties of molecular materials and their aggregates.     

基于微分法精确测量气溶胶飞行时间的新方法

利用飞行时间气溶胶粒子束光谱技术对大气气溶胶牲子粒谱分布进行监测是精确测量大气气溶胶粒子粒径大小及浓度的典型方法.而精确测量气溶胶粒子飞行时间是实现粒径谱精确监测的关键.利用微分法对门限电平比较法进行优化改进,利用信号微分后的零点对应信号最大值的特点,将飞行时间提取中变化的门限电平的比较转换成零电平的比较,设计了一种精确测量气溶胶粒子飞行时间的方法.该方法不但可以忽略因气溶胶粒子大小而引起的散射光强弱变化,而且,即使散射光双峰信号并非理想的对称信号,该方法也能精确地测得飞行时间.