基于电化学老化衰退模型的锂离子动力电池外特性

当前锂离子动力电池电化学模型存在模型复杂、建模难度大、计算效率低、老化评估效果差的问题,本文提出一种考虑电池衰退老化的机理模型(ADME).本文首先通过有限差分法对伪二维(P2D)电化学模型进行离散降阶处理,得到简化伪二维(SP2D)模型.在SP2D模型的基础上,基于阴阳两极发生的副反应导致的衰退老化现象,提出一种考虑电池衰退老化的机理模型.其次,使用多变量偏差补偿最小二乘法实现模型参数辨识.最后通过动力电池衰退老化性能循环实验,对比分析了恒流、脉冲工况下SP2D模型和ADME模型的终端电压输出.结果表明:ADME模型较为简单、计算效率和估算精度高,可以有效评估电池容量老化衰退,得到理想的锂离子动力电池外特性曲线.     

光纤基底TiNi形状记忆合金薄膜制备工艺

将TiNi基记忆合金薄膜与光纤相结合可制成智能化、集成化且成本经济的微机电系统和微传感器件.本文采用磁控溅射法在二氧化硅光纤基底上制备TiNi记忆合金薄膜,系统讨论了溅射工艺参数以及后续退火处理对薄膜质量的影响.采用自研制光纤镀膜掩膜装置在直径为125μm的光纤圆周表面上形成均匀薄膜.实验表明:在靶基距、背底真空度、Ar气流量和溅射时间一定的条件下,溅射功率存在最佳值;溅射压强较大时,薄膜沉积速率较低,但薄膜表面粗糙度较小.进行退火处理后,薄膜形成较良好的晶体结构,Ti_49.09Ni_50.91薄膜中马氏体B19′相和奥氏体B2相共存,但以B19′为主.根据本文研究结果,在玻璃光纤基底上制备高质量的TiNi基记忆合金薄膜是可实现的,本工作为下一步研制微机电系统和微型传感器做了基础准备.     

Mg,N掺杂β-Ga2O3光电性质的第一性原理计算

通过基于密度泛函理论的第一性原理计算,研究了Mg单掺杂、N单掺杂和不同浓度的Mg-N共掺杂β-Ga₂O₃的结构性质、电子性质和光学性质,以期获得性能比较优异的p型β-Ga₂O₃材料。建立了五种模型:Mg单掺杂、N单掺杂、1个Mg-N共掺杂、2个Mg-N共掺杂和3个Mg-N共掺杂β-Ga₂O₃。经过计算,3个Mg-N共掺杂β-Ga₂O₃体系的结构最稳定。此外,在5种模型中,3个Mg-N共掺杂β-Ga₂O₃体系的禁带宽度是最小的,并且N 2p和Mg 3s贡献的占据态抑制了氧空位的形成,从而增加了空穴浓度。因此,3个Mg-N共掺杂β-Ga₂O₃体系表现出优异的p型性质。3个Mg-N共掺杂体系的吸收峰出现明显红移,在太阳盲区的光吸收系数较大,这归因于导带Ga 4s、Ga 4p、Mg 3s向价带O 2p、N 2p的带间电子跃迁。本工作将为p型β-Ga₂O₃日盲光电材料的研究和应用提供理论指导。     

非富勒烯受体DA'D型稠环单元的结构修饰及电池性能研究

近年来,设计和合成高性能非富勒烯受体(NFAs)材料已经成为太阳能电池研究领域的前沿课题。基于DA'D型稠环结构的NFAs由于具有吸光系数高、能级和带隙可调、结构易于修饰、分子可高效合成、光电学性能优异等优点而受到了越来越广泛的关注。在短短7年的时间里,能量转换效率(PCE)从3%~4%提高到18%。2019年初邹应萍等报道了一个优秀的受体分子Y6,与PM6共混制备单结电池,获得了15.7%的能量转换效率。Y6类受体材料的中心给电子单元为DA'D型稠环结构,缺电子单元(A')通过氮原子与两个给电子单元(D)并联形成稠环结构,这有助于降低前线分子轨道能级并增强吸收,同时与氮相连的两个烷基链和位于噻吩并噻吩β位的两个侧链则有助于提高溶解度及调节结晶性。自Y6问世以来,人们对分子的结构剪裁进行了深入的研究,并报道了数十种新的结构。在这些新的受体中,DA'D部分的结构裁剪对提高器件效率和太阳能电池的性能起着至关重要的作用。本文对A'、D单元和侧链结构修饰的研究进展进行了综述。通过选择几组受体,对最近报道的分子进行分类,并将它们的光学、电化学、电学和光电性质与精确的结构修饰相关联,从而对结构-性能关系进行全面概述。     

Predicting 19F NMR Chemical Shifts: A Combined Computational and Experimental Study of a Trypanosomal Oxidoreductase–Inhibitor Complex

The absence of fluorine from most biomolecules renders it an excellent probe for NMR spectroscopy to monitor inhibitor–protein interactions. However, predicting the binding mode of a fluorinated ligand from a chemical shift (or vice versa) has been challenging due to the high electron density of the fluorine atom. Nonetheless, reliable ¹⁹F chemical-shift predictions to deduce ligand-binding modes hold great potential for in silico drug design. Herein, we present a systematic QM/MM study to predict the ¹⁹F NMR chemical shifts of a covalently bound fluorinated inhibitor to the essential oxidoreductase tryparedoxin (Tpx) from African trypanosomes, the causative agent of African sleeping sickness. We include many protein–inhibitor conformations as well as monomeric and dimeric inhibitor–protein complexes, thus rendering it the largest computational study on chemical shifts of ¹⁹F nuclei in a biological context to date. Our predicted shifts agree well with those obtained experimentally and pave the way for future work in this area.     

Contingent derivatives and regularization for noncoercive inverse problems

Abstract

We study the inverse problem of parameter identification in noncoercive variational problems that commonly appear in applied models. We examine the differentiability of the set-valued parameter-to-solution map using the first-order and the second-order contingent derivatives. We explore the inverse problem using the output least-squares and the modified output least-squares objectives. By regularizing the noncoercive variational problem, we obtain a single-valued regularized parameter-to-solution map and investigate its smoothness and boundedness. We also consider optimization problems using the output least-squares and the modified output least-squares objectives for the regularized variational problem. We give a complete convergence analysis showing that for the output least-squares and the modified output least-squares, the regularized minimization problems approximate the original optimization problems suitably. We also provide the first-order and the second-order adjoint method for the computation of the first-order and the second-order derivatives of the output least-squares objective. We provide discrete formulas for the gradient and the Hessian calculation and present numerical results.     

求解矩阵方程AXB+CXD=F参数迭代法的最优参数分析

本文针对求矩阵方程AXB+CXD=F唯一解的参数迭代法，分析当矩阵A，B，C，D均是Hermite正（负）定矩阵时，迭代矩阵的特征值表达式，给出了最优参数的确定方法，并提出了相应的加速算法.     

高居里温度铁电单晶PIN-PT的机电性能

弛豫铁电单晶Pb(In_1/2Nb_1/2)O₃-PbTiO₃(PIN-PT)相较于常用的Pb(Mg_1/3Nb_2/3)O₃-PbTiO₃(PMN-PT)具有更高的居里温度,在高稳定性、高性能的传感器、换能器方面具有应用前景。本工作采用谐振法研究了[001]方向极化的0.66PIN-0.34PT铁电单晶的全矩阵机电性能参数。0.66PIN-0.34PT 单晶的三方-四方相变温度(T_RT)约为160 ℃,居里温度(T_C)约为260 ℃,室温压电系数d₃₃、d₃₁、d₁₅分别为1 340 pC/N、-780 pC/N、321 pC/N,介电常数ε^T₃₃、ε^S₃₃、ε^T₁₁、ε^S₁₁分别为2 700、905、2 210、1 927,机电耦合系数 k₃₃、k₃₁、k₁₅、k_t分别为 87%、58%、38%、61%。其纵向压电常数(d₃₃)和纵向机电耦合系数(k₃₃)小于 PMN-PT 单晶,但是横向压电性能(d₃₁)和剪切压电性能(d₁₅)都略高于PMN-PT单晶。另外,研究了机电耦合性能随温度的变化趋势,发现0.66PIN-0.34PT单晶在150 ℃以下有较好的温度稳定性。     

Direct visualization of structural defects in 2D semiconductors

Direct visualization of the structural defects in two-dimensional (2D) semiconductors at a large scale plays a significant role in understanding their electrical/optical/magnetic properties, but is challenging. Although traditional atomic resolution imaging techniques, such as transmission electron microscopy and scanning tunneling microscopy, can directly image the structural defects, they provide only local-scale information and require complex setups. Here, we develop a simple, non-invasive wet etching method to directly visualize the structural defects in 2D semiconductors at a large scale, including both point defects and grain boundaries. Utilizing this method, we extract successfully the defects density in several different types of monolayer molybdenum disulfide samples, providing key insights into the device functions. Furthermore, the etching method we developed is anisotropic and tunable, opening up opportunities to obtain exotic edge states on demand.