Model chains are studied via Monte Carlo simulations which are deposited with a fixed flux on a substrate. They may represent, e.g., stiff lipophilic chains with an head group and tail groups mimicking the alkyl chain. After some subsequent fixed simulation time we determine the final energy as a function of flux and temperature. Surprisingly, in some range of temperature and flux the final energy increases with decreasing flux. The physical origin of this counterintuitive observation is elucidated. In contrast, when performing equivalent cooling experiments no such anomaly is observed. Furthermore, it is elaborated whether flux experiments give rise to configurations with lower energies as compared to cooling experiments. These results are related to recent experiments by the Ediger group where very stable configurations of glass-forming systems have been generated via flux experiments.
Drastic effect of sequential deposition resulting from flux directionality on the luminescence efficiency of nanowire shells
核-壳纳米线异质结构形成了许多创新器件的基础。当通过定向沉积技术生长复合纳米线壳时，生长反应器中不同组分的源的方位角位置、衬底旋转和纳米线自遮蔽不可避免地导致顺序沉积。在这里，我们发现，在$_{0.15}$ Ga $_{0.85}$ As/GaAs壳层量子阱的分子束外延生长的发光效率的这种顺序性的剧烈影响。采用与迁移增强外延相对应的助熔剂顺序生长的壳层量子阱的光致发光强度。e.当As和III族金属基本上不同时撞击时，比用基本上重叠的通量制备的壳量子阱高两个数量级以上。透射电子显微镜没有发现任何扩展缺陷解释这种差异。我们的光致发光瞬态分析表明，共沉积有两个不利的微观效应。首先，较高密度的电活性点缺陷导致内部电场降低电子-空穴波函数重叠。第二，更多的点缺陷形成，作为非辐射复合中心。我们的研究表明，源的生长反应器，这仅仅是平面结构的技术相关性的安排，可以有严重的后果纳米线壳的材料性能。我们希望这一发现也适用于其他合金纳米线壳。
Amorphous Fe-Sn nanofilms for anomalous-Nernst heat-flux sensing
Toyota Technological Institute（名古屋工业大学）；Graduate School of Engineering, Nagoya Institute of Technology（名古屋工业大学大学院工学研究科）；Quantum-Phase Electronics Center (QPEC) and Department of Applied Physics, The University of Tokyo（东京大学量子相电子中心及应用物理系）；Institute for Materials Research, Tohoku University（东北大学材料科学研究所）；Department of Chemistry, Rikkyo University（立教大学化学系）
The role of ionization fraction on the surface roughness, density and interface mixing of the films deposited by thermal evaporation, dc magnetron sputtering and HiPIMS: An atomistic simulation
Physics-separating artificial neural networks for predicting sputtering and thin film deposition of AlN in Ar/N$_2$ discharges on experimental timescales
Ruhr University Bochum（波鸿鲁尔大学）；Kiel University（基尔大学）
