The demand for graphene-based devices is rapidly growing but there are significant challenges for developing scalable and repeatable processes for the manufacturing of graphene devices. Basic research on understanding and controlling growth mechanisms have recently enabled various mass production approaches over the past decade. However, the integration of graphene with Micro-Nano Electromechanical Systems (MEMS/NEMS) has been especially challenging due to performance sensitivities of these systems to the production process. Therefore, ability to produce graphene-based devices on a large scale with high repeatability is still a major barrier to the commercialization of graphene. In this review article, we discuss the merits of integrating graphene into Micro-Nano Electromechanical Systems, current approaches for the mass production of graphene integrated devices, and propose solutions to overcome current manufacturing limits for the scalable and repeatable production of integrated graphene-based devices.

One of the main challenges facing the expansion of Additive Manufacturing (AM) is the minimum feature sizes which these processes are able to achieve. Microscale Selective Laser Sintering (μ-SLS) is a novel Additive Manufacturing process created to meet this limitation by precisely laser sintering nanoparticles to give a better control over feature sizes. With the development of this new process, there is a concurrent need for models, which can predict the material properties of the sintering nanoparticles. To this end, this paper presents a novel simulation created to predict the electrical resistivity of sintered copper nanoparticles. Understanding the electrical resistivity of nanoparticles under sintering is useful for quantifying the rate of sintering and has applications such as predicting how the nanoparticles will fuse together when subjected to laser irradiation. Such a prediction allows for in situ corrections to be made to the sintering process to account for heat spreading beyond the intended laser irradiation targets. For these applications, it is important to ensure that the predictions of electrical resistivity from the simulations are accurate. This validation must be done against experimental data and since such experimental data does not currently exist, this paper also presents electrical resistivity data for the laser sintering of copper nanoparticles. In summary, this paper details the simulation methodology for predicting electrical resistivity of laser-sintered copper nanoparticles as well as validation of these simulations using electrical resistivity data from original sintering experiments. The key findings of this work are that the simulations can be used to predict electrical resistivity measurements for sintering of actual copper nanoparticles when the copper nanoparticles do not include other materials such as polymer coatings.

Current additive manufacturing (AM) technologies are typically limited by the minimum feature sizes of the parts they can produce. This issue is addressed by the microscale selective laser sintering system (μ-SLS), which is capable of building parts with single micrometer resolutions. Despite the resolution of the system, the minimum feature sizes producible using the μ-SLS tool are limited by unwanted heat dissipation through the particle bed during the sintering process. To address this unwanted heat flow, a particle scale thermal model is needed to characterize the thermal conductivity of the nanoparticle bed during sintering and facilitate the prediction of heat affected zones. This would allow for the optimization of process parameters and a reduction in error for the final part. This paper presents a method for the determination of the effective thermal conductivity of copper nanoparticle beds in a μ-SLS system using finite element simulations performed in ansys. A phase field model (PFM) is used to track the geometric evolution of the particle groups within the particle bed during sintering. Computer aided design (CAD) models are extracted from the PFM output data at various time-steps, and steady-state thermal simulations are performed on each particle group. The full simulation developed in this work is scalable to particle groups with variable sizes and geometric arrangements. The particle thermal model results from this work are used to calculate the thermal conductivity of the copper nanoparticles as a function of the density of the particle group.