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  3. Strongly interacting electrons in solid-state systems often display multiple broken symmetries in the ground state. The interplay between different order parameters can give rise to a rich phase diagram. We report on the identification of intertwined phases with broken rotational symmetry in magic-angle twisted bilayer graphene (TBG). Using transverse resistance measurements, we find a strongly anisotropic phase located in a “wedge” above the underdoped region of the superconducting dome. Upon its crossing with the superconducting dome, a reduction of the critical temperature is observed. Furthermore, the superconducting state exhibits an anisotropic response to a direction-dependent in-plane magnetic field, revealing nematic ordering across the entire superconducting dome. These results indicate that nematic fluctuations might play an important role in the low-temperature phases of magic-angle TBG.

     
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  4. Abstract

    Phonon polaritons in van der Waals materials reveal significant confinement accompanied with long propagation length: important virtues for tasks pertaining to the control of light and energy flow at the nanoscale. While previous studies of phonon polaritons have relied on relatively thick samples, here reported is the first observation of surface phonon polaritons in single atomic layers and bilayers of hexagonal boron nitride (hBN). Using antenna‐based near‐field microscopy, propagating surface phonon polaritons in mono‐ and bilayer hBN microcrystals are imaged. Phonon polaritons in monolayer hBN are confined in a volume about one million times smaller than the free‐space photons. Both the polariton dispersion and their wavelength–thickness scaling law are altered compared to those of hBN bulk counterparts. These changes are attributed to phonon hardening in monolayer‐thick crystals. The data reported here have bearing on applications of polaritons in metasurfaces and ultrathin optical elements.

     
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  5. Abstract

    Advanced microscopy and/or spectroscopy tools play indispensable roles in nanoscience and nanotechnology research, as they provide rich information about material processes and properties. However, the interpretation of imaging data heavily relies on the “intuition” of experienced researchers. As a result, many of the deep graphical features obtained through these tools are often unused because of difficulties in processing the data and finding the correlations. Such challenges can be well addressed by deep learning. In this work, the optical characterization of 2D materials is used as a case study, and a neural‐network‐based algorithm is demonstrated for the material and thickness identification of 2D materials with high prediction accuracy and real‐time processing capability. Further analysis shows that the trained network can extract deep graphical features such as contrast, color, edges, shapes, flake sizes, and their distributions, based on which an ensemble approach is developed to predict the most relevant physical properties of 2D materials. Finally, a transfer learning technique is applied to adapt the pretrained network to other optical identification applications. This artificial‐intelligence‐based material characterization approach is a powerful tool that would speed up the preparation, initial characterization of 2D materials and other nanomaterials, and potentially accelerate new material discoveries.

     
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