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.     

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.     

Metabolite identification of tanshinol borneol ester in rats by high‐performance liquid chromatography combined with electrospray ionization quadrupole time‐of‐flight mass spectrometry

Tanshinol borneol ester (DBZ) is a potential drug candidate composed of danshensu and borneol. It shows anti‐ischemic and anti‐atherosclerosis activity. However, little is known about its metabolism in vivo. This research aimed to elucidate the metabolic profile of DBZ through analyzing its metabolites using high‐performance liquid chromatography combined with electrospray ionization quadrupole time‐of‐flight mass spectrometry. Chromatographic separation was performed on an Agilent TC‐C₁₈ column (150 × 4.6 mm, 5.0 μm) with gradient elution using methanol and water containing 0.2% (v/v) formic acid as the mobile phase. Metabolite identification involved analyzing the retention behaviors, changes in molecular weights and MS/MS fragment patterns of DBZ and its metabolites. As a result, 20 potential metabolites were detected and tentatively identified in rat plasma, urine and feces after administration of DBZ. DBZ could be metabolized to O‐methylated DBZ, DBZ‐O‐glucuronide, O‐methylated DBZ‐O‐glucuronide, hydroxylated DBZ and danshensu. Danshensu, a hydrolysis product of DBZ, could further be transformed into 12 metabolites. The proposed method was confirmed to be a reliable and sensitive alternative for characterizing metabolic pathways of DBZ and providing valuable information on its druggability.     

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.     

Controlled-advancement rigid-body optimization of nanosystems

In this study, we propose a novel optimization algorithm, with application to the refinement of molecular complexes. Particularly, we consider optimization problem as the calculation of quasi-static trajectories of rigid bodies influenced by the inverse-inertia-weighted energy gradient and introduce the concept of advancement region that guarantees displacement of a molecule strictly within a relevant region of conformational space. The advancement region helps to avoid typical energy minimization pitfalls, thus, the algorithm is suitable to work with arbitrary energy functions and arbitrary types of molecular complexes without necessary tuning of its hyper-parameters. Our method, called controlled-advancement rigid-body optimization of nanosystems (Carbon), is particularly useful for the large-scale molecular refinement, as for example, the putative binding candidates obtained with protein–protein docking pipelines. Implementation of Carbon with user-friendly interface is available in the SAMSON platform for molecular modeling at https://www.samson-connect.net . © 2019 Wiley Periodicals, Inc.     

Characterization and tentative identification of new flunitrazepam metabolites in authentic human urine specimens using liquid chromatography‐Q exactive‐HF hybrid quadrupole‐Orbitrap‐mass spectrometry (LC‐QE‐HF‐MS)

Flunitrazepam (FNZ) is a potent hypnotic, sedative, and amnestic drug used to treat severe insomnia. In our recent study, FNZ metabolic profiles were investigated carefully. Six authentic human urine samples were purified using solid phase extraction (SPE) without enzymatic hydrolysis, and urine extracts were then analyzed by liquid chromatography‐Q exactive‐HF hybrid quadrupole‐Orbitrap‐mass spectrometry (LC‐QE‐HF‐MS), using the full scan positive ion mode and targeted MS/MS (ddms2) technique to make accurate mass measurements. There were 25 metabolites, including 13 phase I and 12 phase II metabolites, which were detected and tentatively identified by LC‐QE‐HF‐MS. In addition, nine previously unreported phase II glucuronide conjugates and four phase I metabolites are reported here for the first time. Eight metabolic pathways, including N‐reduction and O‐reduction, N‐glucuronidation, O‐glucuronidation, mono‐hydroxylation and di‐hydroxylation, demethylation, acetylation, and combinations, were implicated in this work, and 2‐O‐reduction together with dihydroxylation were two novel metabolic pathways for FNZ that were identified tentatively. Although 7‐amino FNZ is widely considered to be the primary metabolite, a previously unreported metabolites (M12) can also serve as a potential biomarker for FNZ misuse.     

基于结构性思维的生物化学实验教学探索

为了多维度地培养学生的实践创新能力、终身学习能力及信息获取能力等核心素养，以具体实验为例，探讨结构性思维在生物化学实验教学中的综合应用。教学实践表明：结构性思维可使知识更有序、梯度更明确、更易表达，实验操作变得程序化，学生学习倾向主动，是一种多维度地培养学生核心素养的教学模式。