Growth, charge and thermal transport of flowered graphene

Abstract

We report on the structural and transport properties of the smallest dislocation loop in graphene, known as a flower defect. First, by means of advanced experimental imaging techniques, we deduce how flower defects are formed during recrystallization of chemical vapor deposited graphene. We propose that the flower defects arise from a bulge type mechanism in which the flower domains are the grains left over by dynamic recrystallisation. Next, in order to evaluate the use of such defects as possible building blocks for all-graphene electronics, we combine multiscale modeling tools to investigate the structure and the electron and phonon transport properties of large monolayer graphene samples with a random distribution of flower defects. For large enough flower densities, we find that electron transport is strongly suppressed while, surprisingly, hole transport remains almost unaffected. These results suggest possible applications of flowered graphene for electron energy filtering. For the same defect densities, phonon transport is reduced by orders of magnitude as elastic scattering by defects becomes dominant. Heat transport by flexural phonons, key in graphene, is largely suppressed even for very low concentrations.

High density synthesis of topological point defects in graphene on 6H–SiC(0001¯)

“…One consequence is the generation of small domains isolated from the rest by GBL such as FD or conjoined-twin defects. Containment of small areas within the rest of the environment has already been observed by TEM [12] and a bulge nucleation mechanism has been proposed which has great similarities with our hypothesis. This could be confirmed by molecular dynamic simulations of GB migration [26,29,53] in presence of Au islands.…”

“…Recently, those FD have attracted great interest towards nanotechnological electronic applications. First, they induce very large modifications of transport properties compared to other point-like defects [10]; second, they create a high spin polarization in zigzag graphene nanoribbon [11]; third, they are able to filter electrons from holes, which have applications for electron energy filtering [12]; fourth, they are good candidates for allelectronic valley based-devices [13]. From theoretical point of view, it is also interesting to note that similar flat geometric defects in honeycomb arrangement have been observed on hexagonal bilayer silica [14] and rubrene on Au(111) [15].…”

Modification on Flower Defects and Electronic Properties of Epitaxial Graphene by Erbium

“…It is known that electronic properties of bilayer graphene may depend on the positions of FDs relative to the underlying graphene, and FDs belonging to the same domain are more or less in interaction with others in vicinity and this interaction could induce inhomogeneous stress, which is of great importance to the local mechanical properties. 47 FDs have a wide range of potential applications in nanoelectronics, such as (1) inducing considerable modifications of transport properties compared to other point-like defects; 48 (2) separating electrons from holes and filtering electrons in a certain energy range; 49 (3) creating a high spin polarization in zigzag graphene nanoribbons; 50 and (4) opening a band gap for graphene. 51 Therefore, the generation of FDs with high density is of great significance fortuning graphene electronic properties.…”

“…FDs have a wide range of potential applications in nanoelectronics, such as (1) inducing considerable modifications of transport properties compared to other point-like defects; 48 (2) separating electrons from holes and filtering electrons in a certain energy range; 49 (3) creating a high spin polarization in zigzag graphene nanoribbons; 50 and (4) opening a band gap for graphene. 51 Therefore, the generation of FDs with high density is of great significance fortuning graphene electronic properties.…”

Optimal design of thermoelectric properties of graphene nanoribbons with 5-7 ring defects based on Bayesian algorithm

“…多孔石墨烯超晶格结构的热电性能进行了优化设计；T. M. Dieb 等人 [23] 基于蒙特卡洛树搜索 方法预测了掺硼石墨烯的最稳定构型；王金兰等人 [24] 采用密度泛函理论计算结合机器学习， 发展目标驱动方法成功地预测了性能优异的混合有机无机钙钛矿(HOIPs)光伏材料；袁睿豪等 人 [25] 则利用机器学习算法加速发现了具有较大电子应变的压电材料无铅 BaTiO 3 。目前，遗传 算法、粒子群优化、贝叶斯算法、蒙特卡洛树搜索等各种智能优化算法被广泛用作黑箱优化 工具 [26] ，其中贝叶斯算法更具有通用性及高效性 [27] 。可见机器学习算法不仅加速了具有高性 能物化属性新材料的发现，也为实验指导新材料的合成和制备提供了理论基础。 石墨烯自从 2004 年被成功制备以来，得益于其优异的电学、力学特性引发了科研工作者 们极大的研究兴趣 [28][29][30] 。而高的塞贝克系数和极大的电导率，同样使得石墨烯有望成为优异 的热电材料。遗憾的是，石墨烯也具有极高的热导率(实验表明悬浮单层石墨烯的热导率约 为 3000~5500 W/mK [31,32] )。因此，为了提高石墨烯的热电性质，有效地降低其热导率至关 重要。为了降低石墨烯的热导率，科研工作者们提出通过引入粗糙边界 [33] 、 同位素掺杂 [34,35] 、 构造周期性纳米孔和超晶格结构 [36,37] 、缺陷 [38,39] 等方法来降低石墨烯的热导率。在石墨烯的 制备过程中，缺陷不可避免，而且缺陷的存在会显著影响石墨烯的电学、热学以及机械性能 [40,41] 。在众多缺陷中，5-7 环缺陷是常见的石墨烯缺陷之一。目前该类缺陷能通过 CVD 实验 实现可控制备 [42] 。如图 1 所示，5-7 环缺陷由 7 个碳环围绕其中心环旋转 30 度而成 [42] 。尽管 5-7 环缺陷不会破坏石墨烯中碳原子 sp 2 杂化方式，但该缺陷引入了五元环和七元环，从而打 破了石墨烯的亚晶格对称性。这一特点将会导致电子-空穴不对称并对石墨烯的电子输运性质 产生影响 [42] 本文采用非平衡格林函数来计算体系的电子和声子的输运性质 [43,44] 。 对于电子输运性质， 我们基于紧束缚近似模型构建体系的哈密顿量 [45] ，其电子推迟格林函数可表述为：…”