欢迎访问《电化学（中英文）》期刊官方网站，今天是

电化学, 2022, 28(4): 2105181 doi: 10.13208/j.electrochem.210518

论文

多尺度模拟研究溶质调控下电解液在锂金属电极上的分解机理

张滟滟, 刘越, 陆一鸣, 于沛平, 杜文轩, 麻冰云, 谢淼, 杨昊, 程涛,*

苏州大学功能纳米与软物质学院,苏州 江苏 215123

Multi-Scale Simulation Revealing the Decomposition Mechanism of Electrolyte on Lithium Metal Electrode

Yan-Yan Zhang, Yue Liu, Yi-Ming Lu, Pei-Ping Yu, Wen-Xuan Du, Bing-Yun Ma, Miao Xie, Hao Yang, Tao Cheng,*

Institute of Functional Nano and Soft Materials, Soochow University, Suzhou 215123, Jiangsu, China

通讯作者: *Tel: (86) 15904556170, E-mail:tcheng@suda.edu.cn

收稿日期: 2021-05-18   修回日期: 2021-08-4  

基金资助: 国家自然科学基金项目.  21903058
国家自然科学基金项目.  22003044
江苏省自然科学基金.  编号SBK20190810
江苏省六大人才高峰.  编号JNHB-106
及江苏省高等学校优先发展的学术项目.  PAPD编号

Received: 2021-05-18   Revised: 2021-08-4  

摘要

锂金属具有高比容量和极低的电极电势,被视为下一代高能量密度电池的理想负极材料。然而,锂金属具有很高的活性,在循环过程中会形成锂枝晶,刺穿固态电解质膜（solid electrolyte interphases,SEI）,造成电池短路,引发一系列安全问题,上述缺点极大地阻碍了锂金属的商业应用。为了解决上述问题,理解SEI的结构及其形成原理具有重要意义。在本工作中,我们采用混合从头计算和分子动力学方法（hybrid ab initio and reactive molecule dynamics,HAIR）,研究了1 mol·L-1 LiTFSI-DME（dimethoxyethane）和1 mol·L-1 LiTFSI-EC（ethylene carbonate）两种电解质溶液在锂金属表面的界面反应机理,模拟结果表明,在LiTFSI-DME电解液中,TFSI阴离子优先分解,而DME未见分解,所以TFSI起到了保护DME的作用。但是在LiTFSI-EC体系中,两者均发生了分解,说明EC稳定性较差,不利于形成稳定的SEI,上述模拟结果为通过电解质理性设计开发高性能电解质溶液体系奠定了理论基础。

关键词： 锂金属电池; 固体电解质界面; 多尺度理论模拟; 电化学反应; 电解液设计

Abstract

Lithium metal is considered as an ideal anode material for next-generation high energy density batteries with its high specific capacity and low electrode potential. However, the high activity of lithium metal can lead to a series of safety issues. For example, lithium metal will continuously react chemically with the electrolyte, forming unstable the solid electrolyte (SEI) films. In addition, lithium dendrites can be formed during cycling, which can puncture the SEI film and cause short circuits in the battery. These drawbacks greatly hinder the commercial application of lithium metal. To solve the above problems, it is important to understand the structure of SEI and the underlying mechanism of its formation as a guide for rational design. Quantum mechanics (QM) has been demonstrated as an effective tool to investigate the chemical reactions and microscopic atomic structures of SEI. However, QM is computationally too expensive to be used for large-scale and long-term theoretical simulations. Instead, the molecular mechanics (MM) method has much orders higher computational efficiency than QM, and can be used for large-scale and long-time theoretical simulations. However, the accuracy of MM is usually not guaranteed, especially for complex SEI. Therefore, a practical solution is to combine the advantages of both. In this work, we use the hybrid ab initio and reactive molecule dynamics (HAIRs) approach to describe chemical reactions with the accuracy of quantum chemistry and improve the computational efficiency by more than 10 times with mixing QM and MM. Using this method, we have investigated the interfacial reaction mechanism of two electrolyte solutions, 1 mol·L-1 LiTFSI-DME (dimethoxyethane) and 1 mol·L-1 LiTFSI-EC (ethylene carbonate) with the lithium metal anode. The simulation results show that TFSI anion prefers to be decomposed, while DME does not, thus, TFSI plays the vital role of protecting DME. However, in the LiTFSI-EC system, both TFSI anion and EC are decomposed, indicating that EC is less stable and not suitable to the formation of stable SEI. Thanks to the computational efficiency of the HAIRs method, we have completed the 1 ns simulation in a few days. Using the hardware, the above calculation would take at least one to two months if only the QM method was employed. Meanwhile the long HAIRs calculation shows that for the simulation of chemical reactions in SEI, at least 1 ns is essential. Instead, previous molecular dynamics (MD) simulations with a few ps, or tens of ps, are insufficient to fully capture the critical chemical reactions. The above simulation results provide reliable experience for the computational simulation study of SEI formation, and lay the theoretical foundation for the rational design of electrolytes and the development of high-performance electrolyte solution systems.

Keywords： lithium metal battery; solid electrolyte interface; multi-scale theoretical simulation; electrochemical reaction; electrolyte design

PDF (1019KB) 元数据 多维度评价 相关文章 导出 EndNote| Ris| Bibtex  收藏本文

本文引用格式

张滟滟, 刘越, 陆一鸣, 于沛平, 杜文轩, 麻冰云, 谢淼, 杨昊, 程涛. 多尺度模拟研究溶质调控下电解液在锂金属电极上的分解机理[J]. 电化学, 2022, 28(4): 2105181 doi:10.13208/j.electrochem.210518

Yan-Yan Zhang, Yue Liu, Yi-Ming Lu, Pei-Ping Yu, Wen-Xuan Du, Bing-Yun Ma, Miao Xie, Hao Yang, Tao Cheng. Multi-Scale Simulation Revealing the Decomposition Mechanism of Electrolyte on Lithium Metal Electrode[J]. Journal of Electrochemistry, 2022, 28(4): 2105181 doi:10.13208/j.electrochem.210518

1 引言

可再生能源技术的快速发展提升了对高性能储能设备的需求。锂离子电池（lithium-ion batteries, LIBs）具有高能量密度和合适的操作电压,在各种商业能源存储技术中,尤其是便携式消费电子产品市场中占主导地位[1,2]。目前,商用的锂离子电池主要以石墨为负极,然而随着电池行业的迅速发展,石墨负极的密度逐渐趋近于其理论极限值（372 mAh·g-1）,无法满足迅速发展的市场需求。因此,亟需开发具有更高比能量的电池体系[3,4]。锂金属具有超高的理论比容量（3860 mAh·g-1）和极低的电极电势（-3.040 V,相对于标准氢电极）,引起了科研人员的广泛关注,以金属锂作为负极的锂金属电池 （lithium metal batteries,LMBs）极具竞争力,被视为下一代高能量密度存储器件的必要组成部分[5⇓-7]。

锂金属电池的研究仍然处于起步阶段,其应用仍然存在许多挑战。例如,在充放电循环过程中,由于金属锂的高活性,很容易与电解液发生反应,这些电还原反应导致了固体电解质相界面（solid electrolyte interphases,SEI）的形成。SEI膜成分复杂、结构不均匀并且会诱发体积膨胀。此外,锂的不均匀沉积会导致锂枝晶的形成,锂枝晶的不规则生长会刺穿SEI,导致SEI膜发生破裂,并产生死锂,降低锂金属电池库仑效率,严重阻碍了其商业化发展[7,8]。更严重的是,锂枝晶的生长可能会刺穿隔膜,造成电池内部的短路,导致火灾和爆炸等安全事故,大大缩短了电池的使用寿命[9,10]。为了促进商业锂金属电池性能的优化,全面理解SEI形成过程对于解决金属锂枝晶和金属锂阳极的腐蚀等问题至关重要。不幸的是,SEI层的成分和详细的反应机理还远不够清楚,例如其与电解液的相互作用、锂的沉积等许多关键问题的反应机制认识不足。虽然多种光谱和显微镜工具已被用于探测SEI界面,但原子结构的实验分辨率仍然不够[11,12]。

第一性原理分子动力学（ab initio molecular dynamics, AIMD）模拟已经被证明可以用于理解SEI的初始反应步骤[13],但由于其计算成本过高,模拟时间尺度被限制在几十皮秒（ps）,而阐明SEI膜的形成过程需要纳秒（ns）或者更长,因此当前的模拟尺度还不足以探索SEI膜的形成过程。另一种选择是根据经验方法模拟,如由量子力学（quantum mechanics, QM）得到的反应力场（reactive force field, ReaxFF）[14⇓-16],已经被广泛用于模拟复杂的多相化学反应,计算成本要低得多,这可以将SEI形成的时间尺度扩展到足够大来观察最终的SEI。然而当前ReaxFF的工作框架没有明确考虑电子,这会导致对电化学反应的错误描述。

在本课题中,我们采用了一种混合从头计算和分子动力学方法（hybrid ab initio and reactive molecule dynamics, HAIR）[17,18],这种方法结合了AIMD和ReaxFF分子动力学方法两者的优势。HAIR方法中的AIMD部分可以准确地描述局部电化学反应,而ReaxFF MD可以在保持力场参数训练良好时的QM精度的同时,以更低廉的成本加速化学反应和物质传输过程。在本工作中,我们应用HAIR反应动力学方法,分别探究了LiTFSI和乙二醇二甲醚（dimethoxyethane, DME）以及碳酸乙烯酯（ethylene carbonate, EC）组成的电解液体系在Li（100）表面的分解机理。

2 研究方法

在本工作中,我们以Islam等人开发的ReaxFF参数作为初始数据[19],通过利用精确的量子力学计算产生训练数据,结合蒙特卡洛模拟退火力场优化方法进一步优化参数,得到含双三氟甲磺酰亚胺锂（LiTFSI）电解液体系的力场参数[20,21]。

为了给LiTFSI提供一个QM训练集来训练ReaxFF,我们采用B3LYP杂化密度泛函方法,以及6-311+G（d,p）基组[22]。所有QM计算都是使用Jaguar8.8进行的。在HAIRS模拟中, AIMD和ReaxFF方法分别使用vienna ab initio simulation package（VASP 5.4.4）和large-scale atomic/molecular massively parallel simulator （LAMMPS 2018）软件来模拟锂电解质模型。

对于AIMD模拟,本文采用了广义梯度近似 （GGA）下的Perdew-Burke-Ernzerhof （PBE）函数来描述电子交换和校正能量。为了描述伦敦色散积分,我们添加了Grimme D3校正。另外,在VASP中使用了投影缀加平面波方法,设置了1×1×1 Monkhorst-Pack k空间网格对布里渊区进行积分,来考虑电子-离子相互作用。同时我们选择平面波截断能为400 eV,高斯展开宽度设置为0.2 eV,自洽场的电子结构收敛阈值设置为10-4 eV。

在HAIR步骤中,AIMD和ReaxFF动力学模拟在NVT在300 K下交替进行,固定了分子数（N）,体积（V）和温度（T）。对于两种电解质溶液,我们分别采用1个LiTFSI、 9个DME分子和1个LiTFSI、 14个EC分子来模拟1 mol·L-1 LiTFSI-DME和1 mol·L-1 LiTFSI-EC电解液,两者液相溶剂的密度分别为0.87 g·cm-3和1.32 g·cm-3。对于锂金属阳极,采用6层3×3锂金属的（100）晶面（共计54个Li原子）,其中底部2层锂原子层在模拟中保持固定,初始模型如图1所示。另外,为了保证碰撞和平稳反应的有效收敛,设置AIMD和ReaxFF模拟的步长分别为1 fs和0.25 ps,以此保证在HAIR模拟中的能量守恒所有的HAIR模拟执行了19个循环（104.5 ps）。在本研究中,在扩散速度快的液体电解质上选择了10倍加速度,这意味着分子动力学模拟从AIMD（0.5 ps）开始,接着是ReaxFF MD（5 ps）,然后交替进行。

图1

新窗口打开| 下载原图ZIP| 生成PPT

图1   （A） 1 mol·L-1 LiTFSI-DME电解液的初始模型;（B）1 mol·L-1 LiTFSI-EC电解液的初始模型。其中,紫色代表锂,红色代表氧,黄色代表硫,灰色代表碳,青色代表氟,蓝色代表氮,白色代表氢。（网络版彩图）

Figure 1   Initial models of 1 mol·L-1 (A) LiTFSI-DME and (B) LiTFSI-EC electrolytes. Colors: lithium, purple; oxygen, red; sulfur, yellow; carbon, gray; fluorine, cyan; nitrogen, blue; hydrogen, white. (color on line)


3 结果与讨论

对于DME/LiTFSI电解液,经过19个循环即104.5 ps的HAIR模拟,可以观察到DME未分解,因此在本工作中主要观察LiTFSI的分解过程。如图2所示,图2（A）分别展示了LiTFSI在0 ps,6 ps和11.5 ps时的结构,可以观察到,在第2个循环的AIMD模拟过程中（6 ps）,TFSI开始发生分解,首先是C-S键断裂,得到CF3SO2NSO2和CF3,这与Zhang等人在实验中观察到的结果相一致[23]。接着,在第3个循环的AIMD模拟中（11 ~ 11.5 ps）,CF3SO2NSO2一端的S和O脱落,得到CF3SO2N,于此同时另一组分CF3在这过程中也发生了分解,这一过程的具体分解机理如图2（B）、2（C）所示。CF3SO2NSO2的分解始于11 ps + 25 fs时N-S键的断裂,得到CF3SO2N和SO2两个组分,在11 ps + 110 ps时,SO2的一个O被Li还原,形成Li-O离子键,在11 ps + 110 fs时,另一个O也被还原。另一组分CF3在11 ps + 210 fs时脱去一个F原子,在11 ps + 350 fs时脱去另一个F原子,被Li还原得到LiF。其中, Li2O和LiF已经在实验中被证明是SEI膜的主要组成部分[24,25]。

图2

新窗口打开| 下载原图ZIP| 生成PPT

图2   （A） DME/LiTFSI混合物经HAIR模拟在0 ~ 11.5 ps内得到的TFSI分解过程;（B） CF3SO2NSO2在10 ps ~ 11.5 ps内的分解过程;（C） CF3在10 ps ~ 11.5 ps内的分解过程。其中紫色代表锂,红色代表氧,黄色代表硫,灰色代表碳,青色代表氟,蓝色代表氮,白色代表氢。（网络版彩图）

Figure 2   (A) Sequence of TFSI decompositions obtained from HAIR simulations for the DME/LiTFSI mixture between 0 and 11.5 ps. (B) Sequence of CF3SO2NSO2 decompositions between 11 and 11.5 ps. (C) Sequence of CF3 decompositions between 11 and 11.5 ps. Colors: lithium, purple; oxygen, red; sulfur, yellow; carbon, gray; fluorine, cyan; nitrogen, blue; hydrogen, white. (color on line)


随后,CF3SO2N在第5个循环AIMD模拟过程中（22 ~ 22.5 ps）开始发生分解,如图3所示,首先C-S键发生断裂,生成CF3和SO2N,随后CF3被Li还原。SO2N在第6个循环的AIMD模拟过程中（27.5 ~ 28 ps）失去一个O,在第10个循环的AIMD模拟中（49.5 ~ 50 ps）进一步被还原,再脱去一个O,得到SN。紧接着在下个循环中,C原子靠近NS中的N原子,形成C-N三键,然后S-N键断裂,得到CN,在接下来的循环过程中CN保持稳定不再分解。

图3

新窗口打开| 下载原图ZIP| 生成PPT

图3   CF3SO2N经HAIR模拟在22.5 ~ 104.5 ps内的分解过程。（A） 22.5 ps;（B） 28ps;（C） 50 ps;（D） 55 ps;（E） 55.5 ps;（F） 104.5 ps。其中紫色代表锂,红色代表氧,黄色代表硫,灰色代表碳,青色代表氟,蓝色代表氮,白色代表氢。（网络版彩图）

Figure 3   Sequence of CF3SO2N decompositions obtained from HAIR simulations between 22.5 and 104.5 ps. (A) 22.5 ps. (B) 28 ps. (C) 50 ps. (D) 55 ps. (E) 55.5 ps. (F) 104.5 ps. Color code: lithium, purple;oxygen, red; sulfur, yellow; carbon, gray; fluorine, cyan; nitrogen, blue; hydrogen, white. (color on line)


图4展示了19个循环内LiTFSI的总反应机理,TFSI降解反应始于6 ps时C-S键断裂,形成CF3SO2NSO2和CF3,QM计算和高精度AIMD模拟以及实验结果都与之一致。随后,这些碎片经过多次反应,脱去氟原子或氧原子,主要产物包括LiF和Li2O在过去的实验中已经被证明是SEI层的主要无机成分。在这过程HAIR模拟中没有观察到DME的分解,也与QM计算和实验结果一致。因此,本文提出的HAIR方法在保持AIMD模拟精度的同时,降低了计算成本,同时实验结果也验证了模拟结果的准确性。

图4

新窗口打开| 下载原图ZIP| 生成PPT

图4   由HAIR方法得到的LiTFSI在1 mol·L-1 LiTFSI-DME电解液中总反应路径 （网络版彩图）

Figure 4   Reaction pathway obtained from HAIR simulations for LiTFSI in 1 mol·L-1 LiTFSI-DME electrolyte (color on line)


同样地,我们用相同的方法对含1 mol·L-1 LiTFSI的碳酸乙烯酯（EC）溶液进行了19个循环（104.5 ps）的HAIR模拟。图5（A）展示了EC溶液中TFSI在0 ~ 55.5 ps的分解过程,与DME溶液中TFSI分解机理不同的是,EC溶液中TFSI的分解始于N-S的断裂（6 ps）,接着,在11.5 ps发生C-S键的断裂,得到CF3SO2N、SO2和CF3。与此同时,EC发生了一系列开环反应,在0.5 ps时,一个EC分子经C-O键的断裂形成链状分子,在6 ps时,共10个EC分子发生了反应,6 ps时EC分子的状态如图5（B）所示。由此可见,EC分子的化学稳定性远远不如DME,EC的自发分解会产生一系列可燃气体,如烷烃、烯烃、一氧化碳等,导致电池容量降低,且造成安全隐患。因此使用DME作为溶剂有利于在锂金属电池中建立稳定的界面,提高电池的安全性。

图5

新窗口打开| 下载原图ZIP| 生成PPT

图5   （A）EC/LiTFSI混合物在0-55.5 ps内的分解过程;（B）14个EC溶剂分子在5.5 ps时的状态;其中紫色代表锂,红色代表氧,黄色代表硫,灰色代表碳,青色代表氟,蓝色代表氮,白色代表氢。

Figure 5   (A) Sequence of EC and LiTFSI decompositions obtained from HAIR simulations between 0 and 55.5 ps. (B) The states of 14 EC solvent molecules at 5.5 ps. Colors: lithium, purple; doxygen, red; sulfur, yellow; carbon, gray; fluorine, cyan; nitrogen, blue; hydrogen, white. (color on line)


Balbuena等人[26]运用DFT和AIMD方法研究了锂阳极表面与1 mol·L-1 DME-LiTFSI电解液的反应,发现DME未发生分解,LiTFSI的分解始于C-S键的断裂,这有利于热力学稳定。最终,盐分解成多个碎片。其中最重要的产物是LiF,但其他带电的自由基阴离子来自于C、SO2、O和CSN碎片,它们大多被吸附在金属表面。除此之外, Adri等人[16]利用反应力场（ReaxFF）对碳酸乙烯（EC）自由基阴离子进行了气相和溶液相的反应分子动力学模拟研究,Yun等人[27]利用ReaxFF方法阐明了EC分解反应机理,预测了气体、有机盐和无机盐的生成,与本文预测的反应一致。因此,这些理论结果不仅表明了HAIR方法得到的还原反应机理的可靠性和精确性,而且也证明了HAIR方案可以加速Li的传质,从而节约计算成本。

4 结论

综上所述,本文结合了QM和MM（molecular massively）反应动力学,利用混合从头计算和分子动力学（HAIR）方法研究了三氟甲磺酰亚胺锂（LiTFSI）和乙二醇二甲醚（DME）组成的电解液体系的界面反应及SEI膜形成的机理。我们对体系进行了19个循环（104.5 ps）的HAIR模拟,得到的反应轨迹显示,TFSI的分解首先由C-S键断裂形成CF3SO2NSO2和CF3,接着经N-S键的断裂形成CF3SO2N和SO2,随后氟原子和氧原子被不断还原,逐步从CF3SO2N中分离,生成稳定的CN、LiF和Li2O,其中LiF和Li2O已在实验中被证明是SEI的主要成分。与EC分子在反应中立即发生开环反应不同的是,DME分子在19个循环HAIR模拟中没有发生反应,因此证明了DME分子具有较好的化学稳定性。同时这些理论模拟结果表明,HAIR方法在保持精确度的同时节省了计算成本,有助于阐明SEI形成的反应机理,进而为合理设计电解质以提高锂金属电池的性能提供了更好的方法。

参考文献

Li M, Lu J, Chen Z W, Amine K.

30 Years of lithium-ion batteries

[J]. Adv. Mater., 2018, 30(33): 1800561.

DOI:10.1002/adma.201800561      URL     [本文引用: 1]

Tarascon J M, Armand M.

Issues and challenges facing rechargeable lithium batteries

[J]. Nature, 2001, 414(6861): 359-367.

DOI:10.1038/35104644      URL     [本文引用: 1]

Li P, Hwang J Y, Sun Y K.

Nano/microstructured silicon-graphite composite anode for high-energy-density Li-ion battery

[J]. ACS Nano, 2019, 13(2): 2624-2633.

[本文引用: 1]

Goodenough J B, Park K S.

The Li-ion rechargeable battery: A perspective

[J]. J. Am. Chem. Soc., 2013, 135(4): 1167-1176.

DOI:10.1021/ja3091438      PMID:23294028      [本文引用: 1]

Each cell of a battery stores electrical energy as chemical energy in two electrodes, a reductant (anode) and an oxidant (cathode), separated by an electrolyte that transfers the ionic component of the chemical reaction inside the cell and forces the electronic component outside the battery. The output on discharge is an external electronic current I at a voltage V for a time Δt. The chemical reaction of a rechargeable battery must be reversible on the application of a charging I and V. Critical parameters of a rechargeable battery are safety, density of energy that can be stored at a specific power input and retrieved at a specific power output, cycle and shelf life, storage efficiency, and cost of fabrication. Conventional ambient-temperature rechargeable batteries have solid electrodes and a liquid electrolyte. The positive electrode (cathode) consists of a host framework into which the mobile (working) cation is inserted reversibly over a finite solid-solution range. The solid-solution range, which is reduced at higher current by the rate of transfer of the working ion across electrode/electrolyte interfaces and within a host, limits the amount of charge per electrode formula unit that can be transferred over the time Δt = Δt(I). Moreover, the difference between energies of the LUMO and the HOMO of the electrolyte, i.e., electrolyte window, determines the maximum voltage for a long shelf and cycle life. The maximum stable voltage with an aqueous electrolyte is 1.5 V; the Li-ion rechargeable battery uses an organic electrolyte with a larger window, which increase the density of stored energy for a given Δt. Anode or cathode electrochemical potentials outside the electrolyte window can increase V, but they require formation of a passivating surface layer that must be permeable to Li(+) and capable of adapting rapidly to the changing electrode surface area as the electrode changes volume during cycling. A passivating surface layer adds to the impedance of the Li(+) transfer across the electrode/electrolyte interface and lowers the cycle life of a battery cell. Moreover, formation of a passivation layer on the anode robs Li from the cathode irreversibly on an initial charge, further lowering the reversible Δt. These problems plus the cost of quality control of manufacturing plague development of Li-ion rechargeable batteries that can compete with the internal combustion engine for powering electric cars and that can provide the needed low-cost storage of electrical energy generated by renewable wind and/or solar energy. Chemists are contributing to incremental improvements of the conventional strategy by investigating and controlling electrode passivation layers, improving the rate of Li(+) transfer across electrode/electrolyte interfaces, identifying electrolytes with larger windows while retaining a Li(+) conductivity σ(Li) > 10(-3) S cm(-1), synthesizing electrode morphologies that reduce the size of the active particles while pinning them on current collectors of large surface area accessible by the electrolyte, lowering the cost of cell fabrication, designing displacement-reaction anodes of higher capacity that allow a safe, fast charge, and designing alternative cathode hosts. However, new strategies are needed for batteries that go beyond powering hand-held devices, such as using electrode hosts with two-electron redox centers; replacing the cathode hosts by materials that undergo displacement reactions (e.g. sulfur) by liquid cathodes that may contain flow-through redox molecules, or by catalysts for air cathodes; and developing a Li(+) solid electrolyte separator membrane that allows an organic and aqueous liquid electrolyte on the anode and cathode sides, respectively. Opportunities exist for the chemist to bring together oxide and polymer or graphene chemistry in imaginative morphologies.

Xu W, Wang J L, Ding F, Chen X L, Nasybutin E, Zhang Y H, Zhang J G.

Lithium metal anodes for rechargeable batteries

[J]. Energy Environ. Sci., 2014, 7(2): 513-537.

DOI:10.1039/C3EE40795K      URL     [本文引用: 1]

Lin D C, Liu Y Y, Cui Y.

Reviving the lithium metal anode for high-energy batteries

[J]. Nat. Nanotechnol., 2017, 12(3): 194-206.

DOI:10.1038/nnano.2017.16      URL     [本文引用: 1]

Zheng J M, Engelhard M H, Mei D H, Jiao S H, Polzin B J, Zhang J G, Xu W.

Electrolyte additive enabled fast charging and stable cycling lithium metal batteries

[J]. Nat. Energy, 2017, 2(3): 17012.

DOI:10.1038/nenergy.2017.12      URL     [本文引用: 2]

Huang C, Xiao J, Shao Y Y, Zheng J M, Bennett W D, Lu D P, Saraf L V, Engelhard M, Ji L W, Zhang J G, Li X L, Graff G L, Liu J.

Manipulating surface reactions in lithium-sulphur batteries using hybrid anode structures

[J]. Nat. Commun., 2014, 5: 3015.

DOI:10.1038/ncomms4015      URL     [本文引用: 1]

Xie J, Lu Y C.

A retrospective on lithium-ion batteries

[J]. Nat. Commun, 2020, 11(1): 2499.

DOI:10.1038/s41467-020-16259-9      URL     [本文引用: 1]

Xu X L, Wang S J, Wang H, Hu C, Jin Y, Liu J B, Yan H.

Recent progresses in the suppression method based on the growth mechanism of lithium dendrite

[J]. Energy Chem., 2018, 27(2): 513-527.

[本文引用: 1]

Wang X F, Li Y J, Meng Y S.

Cryogenic electron microscopy for characterizing and diagnosing batteries

[J]. Joule, 2018, 2(11): 2225-2234.

DOI:10.1016/j.joule.2018.10.005      URL     [本文引用: 1]

Nandasiri M I, Camacho-Forero L E, Schwarz A M, Shut-thanandan V, Thevuthasan S, Balbuena P B, Mueller, K T, Murugesan V.

In situ chemical imaging of solid-electrolyte interphase layer evolution in Li-S batteries

[J]. Chem. Mater., 2017, 29(11): 4728-4737.

DOI:10.1021/acs.chemmater.7b00374      URL     [本文引用: 1]

Wang Y M, Liu Y C, Tu Y Q, Wang Q.

Reductive decomposition of solvents and additives toward solid electrolyte interphase formation in lithium-ion battery

[J]. J. Phys. Chem. C, 2020, 124(17): 9099-9108.

DOI:10.1021/acs.jpcc.9b10535      URL     [本文引用: 1]

van Duin A C T, Dasgupta S, Lorant F, Goddard W A.

ReaxFF: A reactive force field for hydrocarbons

[J]. J. Phys. Chem. A, 2001, 105(41): 9396-9409.

DOI:10.1021/jp004368u      URL     [本文引用: 1]

Chenoweth K, van Duin A C T, Goddard W A.

ReaxFF reactive force field for molecular dynamics simulations of hydrocarbon oxidation

[J]. J. Phys. Chem. A, 2008, 112(5): 1040-1053.

DOI:10.1021/jp709896w      PMID:18197648      [本文引用: 1]

To investigate the initial chemical events associated with high-temperature gas-phase oxidation of hydrocarbons, we have expanded the ReaxFF reactive force field training set to include additional transition states and chemical reactivity of systems relevant to these reactions and optimized the force field parameters against a quantum mechanics (QM)-based training set. To validate the ReaxFF potential obtained after parameter optimization, we performed a range of NVT-MD simulations on various hydrocarbon/O2 systems. From simulations on methane/O2, o-xylene/O2, propene/O2, and benzene/O2 mixtures, we found that ReaxFF obtains the correct reactivity trend (propene > o-xylene > methane > benzene), following the trend in the C-H bond strength in these hydrocarbons. We also tracked in detail the reactions during a complete oxidation of isolated methane, propene, and o-xylene to a CO/CO2/H2O mixture and found that the pathways predicted by ReaxFF are in agreement with chemical intuition and our QM results. We observed that the predominant initiation reaction for oxidation of methane, propene, and o-xylene under fuel lean conditions involved hydrogen abstraction of the methyl hydrogen by molecular oxygen forming hydroperoxyl and hydrocarbon radical species. While under fuel rich conditions with a mixture of these hydrocarbons, we observed different chemistry compared with the oxidation of isolated hydrocarbons including a change in the type of initiation reactions, which involved both decomposition of the hydrocarbon or attack by other radicals in the system. Since ReaxFF is capable of simulating complicated reaction pathways without any preconditioning, we believe that atomistic modeling with ReaxFF provides a useful method for determining the initial events of oxidation of hydrocarbons under extreme conditions and can enhance existing combustion models.

Bedrov D, Smith G D, van Duin A C T.

Reactions of singly-reduced ethylene carbonate in lithium battery electrolytes: A molecular dynamics simulation study using the ReaxFF

[J]. J. Phys. Chem. A, 2012, 116(11): 2978-2985.

DOI:10.1021/jp210345b      URL     [本文引用: 2]

Liu Y, Yu P P, Wu Y, Yang H, Xie M, Huai L Y, Goddard W A, Cheng T.

The DFT-ReaxFF hybrid reactive dynamics method with application to the reductive decomposition reaction of the TFSI and DOL electrolyte at a lithium-metal anode surface

[J]. J. Phys. Chem. Lett., 2021, 12(4): 1300-1306.

DOI:10.1021/acs.jpclett.0c03720      URL     [本文引用: 1]

Liu Y, Sun Q T, Yu P P, Wu Y, Xu L, Yang H, Xie M, Cheng T, Goddard W A.

Effects of high and low salt concentrations in electrolytes at lithium-metal anode surfaces using DFT-ReaxFF hybrid molecular dynamics method

[J]. J. Phys. Chem. Lett., 2021, 12(11): 2922-2929.

DOI:10.1021/acs.jpclett.1c00279      URL     [本文引用: 1]

Islam M M, Bryantsev V S, van Duin A C T.

ReaxFF reactive force field simulations on the influence of Teflon on electrolyte decomposition during Li/SWCNT anode discharge in lithium-sulfur batteries

[J]. J. Electrochem. Soc., 2014, 161(8): E3009-E3014.

[本文引用: 1]

Metropolis N, Rosenbluth A W, Rosenbluth M N, Teller A H, Teller E.

Equation of state calculations by fast computing machines

[J]. J. Chem. Phys., 1953, 21(6): 1087-1092.

DOI:10.1063/1.1699114      URL     [本文引用: 1]

Kirkpatrick S, Gelatt C D, Vecchi M P.

Optimization by simulated annealing

[J]. Science, 1983, 220(4598): 671-680.

PMID:17813860      [本文引用: 1]

There is a deep and useful connection between statistical mechanics (the behavior of systems with many degrees of freedom in thermal equilibrium at a finite temperature) and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters). A detailed analogy with annealing in solids provides a framework for optimization of the properties of very large and complex systems. This connection to statistical mechanics exposes new information and provides an unfamiliar perspective on traditional optimization problems and methods.

Becke A D.

Density-functional thermochemistry. III. the role of exact exchange

[J]. J. Chem. Phys., 1993, 98(7): 5648-5652.

DOI:10.1063/1.464913      URL     [本文引用: 1]

Qian J F, Henderson W A, Xu W, Bhattacharya P, Engelhard M, Borodin O, Zhang J G.

High rate and stable cycling of lithium metal anode

[J]. Nat. Commun., 2015, 6: 6362.

DOI:10.1038/ncomms7362      URL     [本文引用: 1]

Li N W, Yin Y X, Yang C P, Guo Y G.

An artificial solid electrolyte interphase layer for stable lithium metal anodes

[J]. Adv. Mater., 2016, 28(9): 1853-1858.

DOI:10.1002/adma.201504526      URL     [本文引用: 1]

Menkin S, Golodnitsky D, Peled E.

Artificial solid-electrolyte interphase (SEI) for improved cycle ability and safety of lithium-ion cells for EV applications

[J]. Electro-chem. Commun., 2009, 11(9): 1789-1791.

[本文引用: 1]

Camacho-Forero L E, Smith T W, Bertolini S, Balbuena P B.

Reactivity at the lithium-metal anode surface of lithium-sulfur batteries

[J]. J. Phys. Chem. C, 2015, 119(48): 26828-26839.

DOI:10.1021/acs.jpcc.5b08254      URL     [本文引用: 1]

Yun K S, Pai S J, Yeo B C, Lee K R, Kim S J, Han S S.

Simulation protocol for prediction of a solid-electrolyte interphase on the silicon-based anodes of a lithium-ion battery: ReaxFF reactive force field

[J]. J. Phys. Chem. Lett., 2017, 8(13): 2812-2818.

DOI:10.1021/acs.jpclett.7b00898      URL     [本文引用: 1]

/

〈 〉

网站版权 © 《电化学（中英文）》编辑部
地址：福建省厦门市思明区厦门大学D信箱　邮编：361005
电话：0592-2181469　 Email：dianhx@xmu.edu.cn
本系统由 北京玛格泰克科技发展有限公司 开发