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Graphene has attracted significant interest both for exploring fundamental science and for a wide range of technological applications. Chemical vapor deposition (CVD) is currently the only working approach to grow graphene at wafer scale, which is required for industrial applications. Unfortunately, CVD graphene is intrinsically polycrystalline, with pristine graphene grains stitched together by disordered grain boundaries, which can be either a blessing or a curse. On the one hand, grain boundaries are expected to degrade the electrical and mechanical properties of polycrystalline graphene, rendering the material undesirable for many applications. On the other hand, they exhibit an increased chemical reactivity, suggesting their potential application to sensing or as templates for synthesis of one-dimensional materials. Therefore, it is important to gain a deeper understanding of the structure and properties of graphene grain boundaries. Here, we review experimental progress on identification and electrical and chemical characterization of graphene grain boundaries. We use numerical simulations and transport measurements to demonstrate that electrical properties and chemical modification of graphene grain boundaries are strongly correlated. This not only provides guidelines for the improvement of graphene devices, but also opens a new research area of engineering graphene grain boundaries for highly sensitive electrobiochemical devices.
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We review our recent work on the physical mechanisms limiting the mobility of graphene on SiO2. We have used intentional addition of charged scattering impurities and systematic variation of the dielectric environment to differentiate the effects of charged impurities and short-range scatterers. The results show that charged impurities indeed lead to a conductivity linear in density in graphene, with a scattering magnitude that agrees quantitatively with theoretical estimates [1]; increased dielectric screening reduces scattering from charged impurities, but increases scattering from short-range scatterers [2]. We evaluate the effects of the corrugations (ripples) of graphene on SiO2 on transport by measuring the height-height correlation function. The results show that the corrugations cannot mimic long-range (charged impurity) scattering effects, and have too small an amplitude-to-wavelength ratio to significantly affect the observed mobility via short-range scattering [3, 4]. Temperature-dependent measurements show that longitudinal acoustic phonons in graphene produce a resistivity linear in temperature and independent of carrier density [5]; at higher temperatures, polar optical phonons of the SiO2 substrate give rise to an activated, carrier density-dependent resistivity [5]. Together the results paint a complete picture of charge carrier transport in graphene on SiO2 in the diffusive regime.
Advances in machine learning have impacted myriad areas of materials science, ranging from the discovery of novel materials to the improvement of molecular simulations, with likely many more important developments to come. Given the rapid changes in this field, it is challenging to understand both the breadth of opportunities as well as best practices for their use. In this review, we address aspects of both problems by providing an overview of the areas where machine learning has recently had significant impact in materials science, and then provide a more detailed discussion on determining the accuracy and domain of applicability of some common types of machine learning models. Finally, we discuss some opportunities and challenges for the materials community to fully utilize the capabilities of machine learning.
The low-temperature thermal conductivity in polycrystalline graphene is theoretically studied. The contributions from three branches of acoustic phonons are calculated by taking into account scattering on sample borders, point defects and grain boundaries. Phonon scattering due to sample borders and grain boundaries is shown to result in a $T^{alpha}$-behaviour in the thermal conductivity where $alpha$ varies between 1 and 2. This behaviour is found to be more pronounced for nanosized grain boundaries. PACS: 65.80.Ck, 81.05.ue, 73.43.Cd
We present a theoretical study of electronic and thermal transport in polycrystalline heterostructures combining graphene (G) and hexagonal boron nitride (hBN) grains of varying size and distribution. By increasing the hBN grain density from a few percents to $100%$, the system evolves from a good conductor to an insulator, with the mobility dropping by orders of magnitude and the sheet resistance reaching the M$Omega$ regime. The Seebeck coefficient is suppressed above $40%$ mixing, while the thermal conductivity of polycrystalline hBN is found to be on the order of $30-120,{rm W}{rm m}^{-1}{rm K}^{-1}$. These results, agreeing with available experimental data, provide guidelines for tuning G-hBN properties in the context of two-dimensional materials engineering. In particular, while we proved that both electrical and thermal properties are largely affected by morphological features (like e.g. by the grain size and composition), we find in all cases that nm-sized polycrystalline G-hBN heterostructures are not good thermoelectric materials.
Motivated by recent experiments on suspended graphene showing carrier mobilities as high as 200,000 cm^2/Vs, we theoretically calculate transport properties assuming Coulomb impurities as the dominant scattering mechanism. We argue that the substrate-free experiments done in the diffusive regime are consistent with our theory and verify many of our earlier predictions including (i) removal of the substrate will increase mobility since most of the charged impurities are in the substrate, (ii) the minimum conductivity is not universal, but depends on impurity concentration with cleaner samples having a higher minimum conductivity. We further argue that experiments on suspended graphene put strong constraints on the two parameters involved in our theory, namely, the charged impurity concentration n_imp and d, the typical distance of a charged impurity from the graphene sheet. The recent experiments on suspended graphene indicate a residual impurity density of 1-2 times 10^{10} cm^{-2} which are presumably stuck to the graphene interface, compared to impurity densities of ~10^{12} cm^{-2} for graphene on SiO_2 substrate. Transport experiments can therefore be used as a spectroscopic tool to identify the properties of the remaining impurities in suspended graphene.
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