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通过分子模拟对烷基硅烷自组装单分子层进行表征。

Characterization of alkylsilane self-assembled monolayers by molecular simulation.

作者信息

Castillo Juan Manuel, Klos Mischa, Jacobs Karin, Horsch Martin, Hasse Hans

机构信息

Laboratory of Engineering Thermodynamics, Department of Mechanical and Process Engineering, University of Kaiserslautern , Erwin-Schrödinger-Str. 44, 67663 Kaiserslautern, Germany.

出版信息

Langmuir. 2015 Mar 10;31(9):2630-8. doi: 10.1021/la504178g. Epub 2015 Feb 23.

Abstract

Self-assembled monolayers (SAM) of dodecyltrichlorosilane (DTS) and octadecyltrichlorosilane (OTS) on silica are studied by molecular dynamics simulations at 298 K and 1 bar. The coverage (number of alkylsilane molecules per surface area) is systematically varied. The results yield insight into the properties of the alkylsilane SAMs, which complement experimental studies from the literature. Relationships are reported between thickness, tilt angle, and coverage of alkylsilane SAMs, which also hold for alkylsilanes other than DTS and OTS. They are interpreted based on the information on molecular ordering in the SAMs taken form the simulation data. System size and simulation time are much larger than in most former simulation works on the topic. This reduces the influence of the initial configuration as well as the periodic boundary conditions and hence minimizes the risk of artificial ordering. At the same time, more reliable statistics for the calculated properties can be provided. The evaluation of experimental data in the field is often based on strongly simplified models. The present simulation results suggest that some of these lead to errors, concerning the interpretation of experimental results, which could be avoided by introducing more realistic models.

摘要

在298K和1巴的条件下,通过分子动力学模拟研究了十二烷基三氯硅烷(DTS)和十八烷基三氯硅烷(OTS)在二氧化硅上的自组装单分子层(SAM)。系统地改变了覆盖率(每表面积的烷基硅烷分子数)。结果深入了解了烷基硅烷自组装单分子层的性质,补充了文献中的实验研究。报道了烷基硅烷自组装单分子层的厚度、倾斜角和覆盖率之间的关系,这些关系也适用于除DTS和OTS之外的烷基硅烷。根据从模拟数据中获得的自组装单分子层中分子排列的信息对其进行了解释。系统规模和模拟时间比以前关于该主题的大多数模拟工作要大得多。这减少了初始构型以及周期性边界条件的影响,从而将人为有序排列的风险降至最低。同时,可以为计算出的性质提供更可靠的统计数据。该领域的实验数据评估通常基于高度简化的模型。目前的模拟结果表明,其中一些模型在解释实验结果时会导致错误,通过引入更现实的模型可以避免这些错误。

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