We investigate the thermal conductivity of suspended graphene as a function of the density of defects, ND, introduced in a controllable way. High-quality graphene layers are synthesized using chemical vapor deposition, transferred onto a transmission electron microscopy grid, and suspended over ∼7.5 μm size square holes. Defects are induced by irradiation of graphene with the low-energy electron beam (20 keV) and quantified by the Raman D-to-G peak intensity ratio. As the defect density changes from 2.0 × 10(10) cm(-2) to 1.8 × 10(11) cm(-2) the thermal conductivity decreases from ∼(1.8 ± 0.2) × 10(3) W mK(-1) to ∼(4.0 ± 0.2) × 10(2) W mK(-1) near room temperature. At higher defect densities, the thermal conductivity reveals an intriguing saturation-type behavior at a relatively high value of ∼400 W mK(-1). The thermal conductivity dependence on the defect density is analyzed using the Boltzmann transport equation and molecular dynamics simulations. The results are important for understanding phonon - point defect scattering in two-dimensional systems and for practical applications of graphene in thermal management.
Conventional metasurfaces have demonstrated efficient wavefront manipulation by using thick and high-aspect-ratio nanostructures in order to eliminate interactions between adjacent phase-shifter elements. Thinner-than-wavelength dielectric metasurfaces are highly desirable because they can facilitate fabrication and integration with both electronics and mechanically tunable platforms. Unfortunately, because their constitutive phase-shifter elements exhibit strong electromagnetic coupling between neighbors, the design requires a global optimization methodology that considers the non-local interactions. Here, we propose a global evolutionary optimization approach to inverse design non-local metasurfaces. The optimal designs are experimentally validated, demonstrating the highest efficiencies for the thinnest transmissive metalenses reported to-date for visible light. In a departure from conventional design methods based on the search of a library of predetermined and independent meta-atoms, we take full advantage of the strong interactions among nanoresonators to improve the focusing efficiency of metalenses and demonstrate that efficiency improvements can be obtained by lowering the metasurface filling factors.
Solution-phase printing of exfoliated graphene flakes is emerging as a low-cost means to create flexible electronics for numerous applications. The electrical conductivity and electrochemical reactivity of printed graphene has been shown to improve with post-print processing methods such as thermal, photonic, and laser annealing. However, to date no reports have shown the manipulation of surface wettability via post-print processing of printed graphene. Herein, we demonstrate how the energy density of a direct-pulsed laser writing (DPLW) technique can be varied to tune the hydrophobicity and electrical conductivity of the inkjet-printed graphene (IPG). Experimental results demonstrate that the DPLW process can convert the IPG surface from one that is initially hydrophilic (contact angle ∼47.7°) and electrically resistive (sheet resistance ∼21 MΩ □) to one that is superhydrophobic (CA ∼157.2°) and electrically conductive (sheet resistance ∼1.1 kΩ □). Molecular dynamic (MD) simulations reveal that both the nanoscale graphene flake orientation and surface chemistry of the IPG after DPLW processing induce these changes in surface wettability. Moreover, DPLW can be performed with IPG printed on thermally and chemically sensitive substrates such as flexible paper and polymers. Hence, the developed, flexible IPG electrodes treated with DPLW could be useful for a wide range of applications such as self-cleaning, wearable, or washable electronics.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.