Frequency Shift and Sub-band Effect in Pair-Production Process Under Adiabatic Closing the External Field

Oscillating electric field is chosen to investigate the electron-positron pair production process by using a quantum kinetic theory and the effective mass model [Phys. Rev. Lett. 112, 050402 (2014)]. The particle yield exhibits a characteristic oscillatory structure which is related to the multi-photon thresholds. The true peak positions are typically slightly above the naive threshold estimate, which is defined as frequency shift. During the numerical calculations, we find the frequency shift can be affected by the system parameters under adiabatic closing the external field, it is worthwhile to study in detail. In this paper, we investigate the frequency shift and the sub-band effect in electron-positron pair production with oscillating electric field. First, a quantum kinetic theory and the effective mass are presented to obtain the frequency shift, the results are fitted very well. And we find the frequency shift and the sub-band effect can be influenced by pulse duration, photon number, and strength of the external field. The frequency shift becomes evident as increases of photon number and the external field strength. The sub-band width is relatively lower at longer pulse duration, higher photon number region, and weaker external field. The results shown in the paper are helpful for understanding multi-photon pair production process in the strong field.     

Effects of multi-arm structure on crystallization and biodegradation of star-shaped poly(epsilon-caprolactone)

Star-shaped PCL with one to five arms were synthesized by ROP. They have the same crystal structure as linear PCL, indicating that the central cores are located in amorphous regions. The influence of arm length and number on crystallization and melting behavior were studied by DSC, POM, and through the Avrami equation. A model is proposed to analyze the enzymatic degradation process of star-shaped polymers. The multi-arm structure and the limitation of the central cores on the chain mobility of each arm are considered to be the primary factors influencing the characteristic crystallization and enzymatic degradation behavior of star-shaped PCLs.     

Chemical constituents and quality control of two Dracocephalum species based on high‐performance liquid chromatographic fingerprints coupled with tandem mass spectrometry and chemometrics

Two similar Dracocephalum species, namely, Dracocephalum tanguticum Maxim and Dracocephalum moldavica L, are commonly used as ethnic medicines in China. Here we describe a strategy of combining high‐performance liquid chromatography with quadrupole time‐of‐flight mass spectrometry, as well as fingerprints and chemometrics for characterization and discrimination of chemical constituents on the two herbs. A total of 49 compounds including 33 flavonoids, 5 phenylethanol glycosides, 1 coumarin glycoside, 8 organic acids, and 2 other types of compounds were unambiguously or tentatively identified from the two Dracocephalum species. Among the compounds identified, 26 were characterized for the first time and 4 compounds, rosmarinic acid ( 7 ), salvianolic acid B ( 10 ), luteoloside ( 22 ), diosmetin‐7‐O‐glucoside ( 28 ), were inferred as common constituents for the two herbs. Flavonoids featured in these two Dracocephalum species while their types presented significant differences. Acacetin ( 45 ) and acacetin glycosides (acatetin‐7‐O‐glucuronide ( 30 ), acacetin‐7‐O‐(6”‐O‐malonyl) glucoside ( 33 ), buddleoside ( 34 ), tilianin ( 35 ), and agastachoside ( 42 )) were detected only in D. moldavica, which can be used to discriminate two herbal medicines. In addition, six characteristic constitutes in D. tanguticum were simultaneously quantified. Moreover, the induced chemometrics methods including similarity analysis and hierarchical clustering analysis were successfully applied for origin discrimination and quality evaluation of D. tanguticum and D. moldavica.     

TSNet: predicting transition state structures with tensor field networks and transfer learning

Transition states are among the most important molecular structures in chemistry, critical to a variety of fields such as reaction kinetics, catalyst design, and the study of protein function. However, transition states are very unstable, typically only existing on the order of femtoseconds. The transient nature of these structures makes them incredibly difficult to study, thus chemists often turn to simulation. Unfortunately, computer simulation of transition states is also challenging, as they are first-order saddle points on highly dimensional mathematical surfaces. Locating these points is resource intensive and unreliable, resulting in methods which can take very long to converge. Machine learning, a relatively novel class of algorithm, has led to radical changes in several fields of computation, including computer vision and natural language processing due to its aptitude for highly accurate function approximation. While machine learning has been widely adopted throughout computational chemistry as a lightweight alternative to costly quantum mechanical calculations, little research has been pursued which utilizes machine learning for transition state structure optimization. In this paper TSNet is presented, a new end-to-end Siamese message-passing neural network based on tensor field networks shown to be capable of predicting transition state geometries. Also presented is a small dataset of S_N2 reactions which includes transition state structures – the first of its kind built specifically for machine learning. Finally, transfer learning, a low data remedial technique, is explored to understand the viability of pretraining TSNet on widely available chemical data may provide better starting points during training, faster convergence, and lower loss values. Aspects of the new dataset and model shall be discussed in detail, along with motivations and general outlook on the future of machine learning-based transition state prediction.

Transition states are among the most important molecular structures in chemistry, critical to a variety of fields such as reaction kinetics, catalyst design, and the study of protein function.     

Singly diagonally implicit runge‐kutta method for time‐dependent reaction‐diffusion equation

In this article, an efficient fourth‐order accurate numerical method based on Padé approximation in space and singly diagonally implicit Runge‐Kutta method in time is proposed to solve the time‐dependent one‐dimensional reaction‐diffusion equation. In this scheme, we first approximate the spatial derivative using the second‐order central finite difference then improve it to fourth‐order by applying Padé approximation. A three stage fourth‐order singly diagonally implicit Runge‐Kutta method is then used to solve the resulting system of ordinary differential equations. It is also shown that the scheme is unconditionally stable, and is suitable for stiff problems. Several numerical examples are solved by the scheme and the efficiency and accuracy of the new scheme are compared with two widely used high‐order compact finite difference methods. © 2010 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq 27: 1423–1441, 2011     

金属双边二次电子倍增的理论分析与数值模拟

 针对金属双边二次电子倍增现象,分析给出了电子共振方程、共振相位、相位聚焦条件以及碰撞电势;并根据二次电子发射的材料特性,研究了金属双边二次电子倍增的敏感区间。利用蒙特卡罗方法抽样选取电子初始发射能量和角度,数值研究了二次电子倍增的敏感区间,并与理论结果进行了比对,给出了二次电子数目随时间的增长关系。利用材料二次发射特性的经验公式,辅以电子碰撞角和碰撞能量计算以及对二次电子初始能量和发射角度的蒙特卡罗随机抽样算法,编制了3维全电磁粒子模拟程序NEPTUNE的金属边界二次电子发射功能模块,模拟金属双平板二次电子倍增过程,获得了二次电子倍增物理图像、二次电子数目随时间演化规律等结果。模拟结果不仅验证了理论分析,还表明在合适的条件下,空间电荷限制作用将导致二次电子倍增的饱和。     

新型β-二酮铕配合物的合成和性能研究

以肉桂酸和苯乙酮为原料合成了1,5-二苯基-4-烯-1,3-戊二酮（DPPD）配体,以DPPD为第一配体,1,10-邻菲罗啉为第二配体,合成了稀土铕配合物并通过红外光谱进行了表征,同时,研究了配合物的发光性能。     

Study on effective probe depth of optical coherence tomography system by Monte Carlo simulation

The intensities of Class I and Class II signals at several different probing depths and different coherence lengths are analyzed using a Monte Carlo model with a pencil beam, and the result shows that the longitudinal resolution has influence on the effective probe depth. A Monte Carlo model for optical coherence tomography system with a focused Gaussian beam is proposed. The intensities of Class I and Class II signals at different probing depth for different radius and depth of focus of the beam are simulated using this model. We found that increasing of the depth of focus and decreasing of the beam radius can finitely increase effective probing depth. When the effective probing depth is fixed, optimal signal intensity can be achieved by altering the beam radius or the depth of focus.