堪为物理学第三分支的计算物理

本文是作者1982年在中国访问时所作的讲演,论述物理学各领域的前沿工作,用许多事例说明计算物理对推动物理理论的重大意义。文中还指出计算物理在其性质、方法及需要等方面不同于和独立于解析的理论物理和实验物理,而成为物理学的第三分支。     

A picosecond widely tunable deep-ultraviolet laser for angle-resolved photoemission spectroscopy

We develop a picosecond widely tunable laser in a deep-ultraviolet region from 175 nm to 210 nm,generated by two stages of frequency doubling of a 80-MHz mode-locked picosecond Ti:sapphire laser.A β-BaB2O4 walk-off compensation configuration and a KBe2BO3F2 prism-coupled device are adopted for the generation of second harmonic and fourth harmonics,respectively.The highest power is 3.72 mW at 193 nm,and the fluctuation at 2.85 mW in 130 min is less than ±2%.     

Dark lump excitations in superfluid Fermi gases

We study the linear and nonlinear properties of two-dimensional matter-wave pulses in disk-shaped superfluid Fermi gases.A Kadomtsev-Petviashvili I(KPI) solitary wave has been realized for superfluid Fermi gases in the limited cases of Bardeen-Cooper-Schrieffer(BCS) regime,Bose-Einstein condensate(BEC) regime,and unitarity regime.Onelump solution as well as one-line soliton solutions for the KPI equation are obtained,and two-line soliton solutions with the same amplitude are also studied in the limited cases.The dependence of the lump propagating velocity and the sound speed of two-dimensional superfluid Fermi gases on the interaction parameter are investigated for the limited cases of BEC and unitarity.     

量子阱层和垒层具有不同Al组分的270/290/330nm AlGaN基深紫外LED光电性能

为了研究AlGaN量子阱层和垒层中Al组分不同对AlGaN基深紫外发光二极管(LED)光电性能的影响,本文利用MOCVD生长、光刻和干法刻蚀工艺制备了AlGaN量子阱层和垒层具有不同Al组分的270/290/330nm深紫外LED,通过实验和数值模拟计算方法发现,量子阱层和垒层中具有低Al组分紫外LED的AlGaN材料具有较低的位错密度、较高的光输出功率和外量子效率。通过电流-电压(I-V)曲线拟合出的较大的理想因子(3.5)和能带结构图表明,AlGaN深紫外LED的电流产生是隧穿机制占据主导作用,这是因为高Al组分AlGaN量子阱中强极化场造成了有源层区域较大的能带弯曲和电势降。     

优化MGM(1,n)模型及其应用研究

从最小二乘估计的适用条件出发,在建立MGM(1,n)模型前,先采用BoxCox变换对原始数据进行变换,以解决或缓解误差项的正态性偏离问题.首先,以模型拟合平均误差最小化为目标,建立非线性优化模型,在参数的置信区间内,应用PSO(Particle Swarm Optimization)算法求最优解.然后,应用求解得到的参数,建立优化MGM(1,n)模型.在实例分析中,将优化MGM(1,n)模型应用于深基坑围护结构变形预测,实验结果表明通过对原始数据做合适的Box-Cox变换,能够有效提高模型的拟合及预测精度,拓广多变量灰色预测模型的适用范围.     

Metal-free and base-free decarboxylation of substituted cinnamic acids in a deep eutectic solvent

Abstract

A metal-free and base-free strategy was developed in DES to synthesize styrenes for the first time by decarboxylation of cinnamic acid derivatives, which provided a renewable and cost efficiently protocol to access various styrenes including those with functional groups such as 4-vinylphenol and 1-chloro-4-vinylbenzene.     

Deep-Eutectic-Solvent-Based Self-Healing Polymer Electrolyte for Safe and Long-Life Lithium-Metal Batteries

The deployment of high-energy-density lithium-metal batteries has been greatly impeded by Li dendrite growth and safety concerns originating from flammable liquid electrolytes. Herein, we report a stable quasi-solid-state Li metal battery with a deep eutectic solvent (DES)-based self-healing polymer (DSP) electrolyte. This electrolyte was fabricated in a facile manner by in situ copolymerization of 2-(3-(6-methyl-4-oxo-1,4-dihydropyrimidin-2-yl)ureido)ethyl methacrylate (UPyMA) and pentaerythritol tetraacrylate (PETEA) monomers in a DES-based electrolyte containing fluoroethylene carbonate (FEC) as an additive. The well-designed DSP electrolyte simultaneously possesses non-flammability, high ionic conductivity and electrochemical stability, and dendrite-free Li plating. When applied in Li metal batteries with a LiMn₂O₄ cathode, the DSP electrolyte effectively suppressed manganese dissolution from the cathode and enabled high-capacity and a long lifespan at room and elevated temperatures.     

深共融溶剂中合成2,4-二取代喹啉衍生物

在氯化胆碱和氯化锌组成的深共融溶剂中,以2-氨基苯乙酮和芳香炔烃为原料,通过环化偶联反应,合成了一系列2,4-二取代喹啉衍生物;当n(氯化胆碱)∶n(氯化锌)=1∶2,反应温度为80℃时,反应3 h即获得高达98%的产率.该方法无需额外添加催化剂,而且反应条件温和、操作简单、底物范围较广泛.     

人工智能助力当代化学研究

以机器学习为代表的人工智能在当代的科学研究中正在发挥越来越重要的作用.不同于传统的计算机程序,机器学习人工智能可以通过对大量数据的反复分析和自身模型的优化,即“学习”过程,从而在大量的数据中寻找客观事物的相互联系,形成具有更好预测和决策能力的新模型,做出合理的判断.化学研究的特点恰恰是机器学习人工智能的强项.化学研究经常要面对十分复杂的物质体系和实验过程,从而很难通过化学物理原理进行精准的分析和判断.人工智能可以挖掘化学实验中产生的海量实验数据的相关性,帮助化学家做出合理分析预测,大大加速化学研发过程.本文介绍了当代人工智能方法及用其解决化学问题基本原理,并通过具体案例展示了人工智能辅助解决不同化学研发问题的方法以及对应的机器学习算法.将人工智能运用在化学科学的尝试正处于蓬勃上升期,人工智能已经初步展示出对化学研究的强大助力,希望本文能帮助更多的国内的化学工作者了解和运用这一有力的工具.