اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدComment on Atomic Scale Structure and Chemical Composition across Order-Disorder Interfaces
218
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
Interfaces have long been known to be the key to many mechanical and electric properties. To nickel base superalloys which have perfect creep and fatigue properties and have been widely used as materials of turbine blades, interfaces determine the strengthening capacities in high temperature. By means of high resolution scanning transmission electron microscopy (HRSTEM) and 3D atom probe (3DAP) tomography, Srinivasan et al. proposed a new point that in nickel base superalloys there exist two different interfacial widths across the {gamma}/{gamma} interface, one corresponding to an order-disorder transition, and the other to the composition transition. We argue about this conclusion in this comment.
قيم البحث
اقرأ أيضاً
Order-disorder processes fundamentally determine the structure and properties of many important oxide systems for energy and computing applications. While these processes have been intensively studied in bulk materials, they are less investigated and
understood for nanostructured oxides in highly non-equilibrium conditions. These systems can now be realized through a range of deposition techniques and probed at exceptional spatial and chemical resolution, leading to a greater focus on interface dynamics. Here we survey a selection of recent studies of order-disorder behavior at thin film oxide interfaces, with a particular emphasis on the emergence of order during synthesis and disorder in extreme irradiation environments. We summarize key trends and identify directions for future study in this growing research area.
The interfacial screening charge that arises to compensate electric fields of dielectric or ferroelectric thin films is now recognized as the most important factor in determining the capacitance or polarization of ultrathin ferroelectrics. Here we in
vestigate using aberration-corrected electron microscopy and density functional theory how interfaces cope with the need to terminate ferroelectric polarization. In one case, we show evidence for ionic screening, which has been predicted by theory but never observed. For a ferroelectric film on an insulating substrate, we found that compensation can be mediated by interfacial charge generated, for example, by oxygen vacancies.
Correlating 3D arrangements of atoms and defects with material properties and functionality forms the core of several scientific disciplines. Here, we determined the 3D coordinates of 6,569 iron and 16,627 platinum atoms in a model iron-platinum nano
particle system to correlate 3D atomic arrangements and chemical order/disorder with material properties at the single-atom level. We identified rich structural variety and chemical order/disorder including 3D atomic composition, grain boundaries, anti-phase boundaries, anti-site point defects and swap defects. We show for the first time that experimentally measured 3D atomic coordinates and chemical species with 22 pm precision can be used as direct input for first-principles calculations of material properties such as atomic magnetic moments and local magnetocrystalline anisotropy. This work not only opens the door to determining 3D atomic arrangements and chemical order/disorder of a wide range of nanostructured materials with high precision, but also will transform our understanding of structure-property relationships at the most fundamental level.
Amorphous materials are coming within reach of realistic computer simulations, but new approaches are needed to fully understand their intricate atomic structures. Here, we show how machine-learning (ML)-based techniques can give new, quantitative ch
emical insight into the atomic-scale structure of amorphous silicon (a-Si). Based on a similarity function (kernel), we define a structural metric that unifies the description of nearest- and next-nearest-neighbor environments in the amorphous state. We apply this to an ensemble of a-Si networks, generated in melt-quench simulations with an ML-based interatomic potential, in which we tailor the degree of ordering by varying the quench rates down to $10^{10}$ K/s (leading to a structural model that is lower in energy than the established WWW network). We then show how machine-learned atomic energies permit a chemical interpretation, associating coordination defects in a-Si with distinct energetic stability regions. The approach is straightforward and inexpensive to apply to arbitrary structural models, and it is therefore expected to have more general significance for developing a quantitative understanding of the amorphous state.
Reply to ``Comment on [Phys. Rev. Lett. 81, 630 (1998)]
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
