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Get Free AccessIn a stack of atomically thin van der Waals layers, introducing interlayer twist creates a moiré superlattice whose period is a function of twist angle. Changes in that twist angle of even hundredths of a degree can dramatically transform the system's electronic properties. Setting a precise and uniform twist angle for a stack remains difficult; hence, determining that twist angle and mapping its spatial variation is very important. Techniques have emerged to do this by imaging the moiré, but most of these require sophisticated infrastructure, time-consuming sample preparation beyond stack synthesis, or both. In this work, we show that torsional force microscopy (TFM), a scanning probe technique sensitive to dynamic friction, can reveal surface and shallow subsurface structure of van der Waals stacks on multiple length scales: the moirés formed between bi-layers of graphene and between graphene and hexagonal boron nitride (hBN) and also the atomic crystal lattices of graphene and hBN. In TFM, torsional motion of an Atomic Force Microscope (AFM) cantilever is monitored as it is actively driven at a torsional resonance while a feedback loop maintains contact at a set force with the sample surface. TFM works at room temperature in air, with no need for an electrical bias between the tip and the sample, making it applicable to a wide array of samples. It should enable determination of precise structural information including twist angles and strain in moiré superlattices and crystallographic orientation of van der Waals flakes to support predictable moiré heterostructure fabrication.
Mihir Pendharkar, Steven J. Tran, Gregory Zaborski, Joe Finney, Aaron L. Sharpe, Rupini Kamat, Sandesh S. Kalantre, M. B. Hocking, Nathan J. Bittner, Kenji Watanabe, Takashi Taniguchi, Bede Pittenger, Christina J. Newcomb, M. A. Kastner, Andrew J. Mannix, David Goldhaber‐Gordon (2024). Torsional force microscopy of van der Waals moirés and atomic lattices. , 121(10), DOI: https://doi.org/10.1073/pnas.2314083121.
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Type
Article
Year
2024
Authors
16
Datasets
0
Total Files
0
Language
en
DOI
https://doi.org/10.1073/pnas.2314083121
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