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Anomalous scaling of flexural phonon damping in nanoresonators with confined fluid 被引量:1

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摘要 Various one and two-dimensional(1D and 2D)nanomaterials and their combinations are emerging as next-generation sensors because of their unique opto-electro-mechanical properties accompanied by large surface-to-volume ratio and high quality factor.Though numerous studies have demonstrated an unparalleled sensitivity of these materials as resonant nanomechanical sensors under vacuum isolation,an assessment of their performance in the presence of an interacting medium like fluid environment is scarce.Here,we report the mechanical damping behavior of a 1D singlewalled carbon nanotube(SWCNT)resonator operating in the fundamental flexural mode and interacting with a fluid environment,where the fluid is placed either inside or outside of the SWCNT.A scaling study of dissipation shows an anomalous behavior in case of interior fluid where the dissipation is found to be extremely low and scaling inversely with the fluid density.Analyzing the sources of dissipation reveals that(i)the phonon dissipation remains unaltered with fluid density and(ii)the anomalous dissipation scaling in the fluid interior case is solely a characteristic of the fluid response under confinement.Using linear response theory,we construct a fluid damping kernel which characterizes the hydrodynamic force response due to the resonant motion.The damping kernel-based analysis shows that the unexpected behavior stems from time dependence of the hydrodynamic response under nanoconfinement.Our systematic dissipation analysis helps us to infer the origin of the intrinsic dissipation.We also emphasize on the difference in dissipative response of the fluid under nanoconfinement when compared to a fluid exterior case.Our finding highlights a unique feature of confined fluid–structure interaction and evaluates its effect on the performance of high-frequency nanoresonators.
出处 《Microsystems & Nanoengineering》 EI CSCD 2019年第1期682-694,共13页 微系统与纳米工程(英文)
基金 The authors gratefully acknowledge the support by National Science Foundation under Grants 1420882 and 1506619 The authors acknowledge the Texas Advanced Computing Center(TACC)at The University of Texas at Austin for providing HPC resources that have contributed to the research results reported within this paper This work also made use of the Illinois Campus Cluster,a computing resource that is operated by the Illinois Campus Cluster Program(ICCP)in conjunction with the National Center for Supercomputing Applications(NCSA) which is supported by funds from the University of Illinois at Urbana-Champaign.
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