Strength and Reliability of Graphene Produced Using Industrially Scalable Methods

石墨烯--碳原子的单原子层--是一种具有许多特殊性质的二维材料。例如，原始无缺陷石墨烯具有任何已知材料中最高的导电性以及最大的机械强度。 为了利用这些独特的特性，科学家和工程师已经使用纳米纤维方法从石墨烯中制造了许多不同的电气，光学和磁性设备。 这些装置中的许多在性能和/或能量使用方面比它们的微尺度对应物优越得多上级。 因此，石墨烯具有巨大的潜力对社会产生积极的影响。然而，原始的无缺陷石墨烯是非常昂贵的，因为必须使用手动方法来分离和操作它。为了解决这个问题，最近已经证明了几种以工业规模化的方式生长石墨烯的方法。 通过这种方法生长的石墨烯将受益于规模经济，这可以转化为利用石墨烯独特性质的纳米制造设备的大规模生产。 然而，通过这些方法生长的石墨烯含有降低性能，特别是机械性能的缺陷。 我们的项目解决了量化通过工业可扩展方法生长的石墨烯的强度和可靠性的根本挑战。 该项目的成果预计将是：（1）量化石墨烯强度和可靠性的实验方法;（2）了解如何优化生长过程，以最大限度地提高石墨烯的强度和可靠性;（3）实验验证的多尺度理论和计算工具，以预测石墨烯的强度和可靠性;以及（4）证明了生长的石墨烯可以用作抗弯强度层压复合材料的骨架。大面积CVD石墨烯的可用性具有众所周知的特性，将使石墨烯基器件的大规模生产成为可能，例如更小更快的电子器件和超高强度复合材料。 此外，PI和支持的学生将与公立纽约市学校系统的高中教师互动，接待学生参观，并开发实验室实验，以测量适合高中科学项目的材料的机械响应。目的是量化CVD石墨烯在给定应力下的失效概率及其平均强度。将采用非均匀应力状态的威布尔概率分布。实验方法将通过纳米压痕和压力加载的独立的圆形膜的CVD石墨烯。 为了使实验结果合理化，将开发石墨烯中晶界的多长度本构模型。 分子动力学模拟将预测以前使用透射电子显微镜（TEM）在原子长度尺度上表征的单个晶界的强度。 这些信息将被转移到连续体内聚区模型，该模型将被纳入详细的有限元计算模型。因此，该模型还将严格考虑多晶CVD石墨烯中各个晶粒的非线性和各向异性特性。 该模型的实验验证的基于物理的预测能力将作为优化石墨烯和其他二维材料的CVD生长的工具。

Graphene--a single atomic layer of carbon atoms--is a two-dimensional material with many exceptional properties. For example, pristine defect-free graphene has the highest electrical conductivity as well as the greatest mechanical strength of any known material. To harness these unique properties, scientists and engineers have fabricated many different electrical, optical and magnetic devices from graphene using nanofabrication methods. Many of these devices are much superior to their microscale counterparts in terms of performance and/or energy usage. Thus graphene has tremendous potential to impact society positively. However pristine defect-free graphene is extremely expensive because manual methods must be used to isolate and manipulate it. To address this, several methods to grow graphene in an industrially scalable manner have been demonstrated recently. Graphene grown by such methods would benefit from an economy of scale that could translate to the mass production of nanofabricated devices that take advantage of graphene's unique set of properties. However graphene grown by these methods contains defects that degrade the properties, especially the mechanical properties. Our project addresses the fundamental challenge of quantifying the strength and reliability of graphene grown by industrially scalable methods. The outcome of the project is expected to be: (1) an experimental method to quantify the strength and reliability of as-grown graphene; (2) an understanding of how the growth process can be optimized to maximize the strength and reliability of as-grown graphene; (3) an experimentally validated multiscale theoretical and computational tool to predict the strength and reliability of graphene; and, (4) demonstration that as-grown graphene can be used as the backbone for ultrahigh strength laminate composites. The availability of large area CVD graphene with well-understood properties will make possible the mass production of graphene-based devices such as ever smaller and faster electronic devices and ultra high strength composite materials. In addition, the PIs and supported student will interact with high school teachers in the public New York City school system to host student visits and to develop laboratory experiments to measure the mechanical response of materials fitting high school science projects. The objective is to quantify CVD graphene's probability of failure at a given stress as well as its mean strength. The Weibull probability distribution for a heterogeneous stress state will be employed. The experimental methodology will be via nanoindentation and pressure loading of free-standing circular films of CVD graphene. In order to rationalize the experimental results, a multiple length constitutive model of grain boundaries in graphene will be developed. Molecular dynamics simulations will predict the strength of individual grain boundaries that were previously characterized at the atomic length scale using Transmission Electron Microscopy (TEM). This information will be transferred to continuum cohesive zone models that will be incorporated into detailed finite element computational models. Thereby, the model will also account rigorously for the non-linear and anisotropic properties of the individual grains in the polycrystalline CVD graphene. The experimentally validated physics-based predictive capability of the model will serve as a tool to optimize the CVD growth of graphene and other two-dimensional materials.