Kaikui Xu
5 Papers
Kaikui Xu is an academic researcher. The author has contributed to research in topics: Plasmon & Geology. The author has an hindex of 1, co-authored 1 publications.
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Papers
Large-Area Periodic Arrays of Atomically Flat Single-Crystal Gold Nanotriangles Formed Directly on Substrate Surfaces.
Robert D. Neal,Zachary R Lawson,Walker J Tuff,Kaikui Xu,Vishal Kumar,Matiyas Tsegay Korsa,Maksym Zhukovskyi,Matthew R. Rosenberger,Jost Adam,Jordan A. Hachtel,Jon P. Camden,Robert A. Hughes,Svetlana Neretina +12 more
TL;DR: In this article , a benchtop process is presented for the formation of large-area periodic arrays of gold nanotriangles, which are epitaxially aligned with the underlying substrate, grown to thicknesses that are not readily obtainable in colloidal syntheses, and present atomically flat pristine surfaces exhibiting gold atoms with a close packed structure.
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Atomic Defect Quantification by Lateral Force Microscopy.
Yucheng Yang,Kaikui Xu,Luke Holtzman,Kristyna Yang,Kenji Watanabe,T. Taniguchi,James Hone,Katayun Barmak,Matthew R Rosenberger +8 more
TL;DR: LFM can characterize atomic defects in both conductive and insulating 2D materials, enabling routine defect-property determination and accelerating materials research.
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Large-Area Arrays of Epitaxially Aligned Silver Nanotriangles Seeded by Gold Nanostructures
Zachary R Lawson,Kaikui Xu,Christina Boukouvala,Robert A. Huhges,Matthew Rosenberger,Emilie Ringe,Svetlana Neretina +6 more
TL;DR: High-level control over colloidal synthesis achieved by separating nucleation and growth processes into distinct steps.
Validating the Use of Conductive Atomic Force Microscopy for Defect Quantification in 2D Materials.
Kaikui Xu,Madisen A. Holbrook,Luke Holtzman,Abhay N Pasupathy,Katayun Barmak,J. Hone,Matthew R. Rosenberger +6 more
TL;DR: CAFM successfully validates its use for defect quantification in 2D materials, achieving single-atom resolution and comparable accuracy to STM.
Detecting Atomic Scale Surface Defects in STM of TMDs with Ensemble Deep Learning
Darian Smalley,Stephanie Lough,Luke Holtzman,Kaikui Xu,Madisen A. Holbrook,Matthew R. Rosenberger,J. Hone,Katayun Barmak,Masahiro Ishigami Department of Physics,U. Florida,Nanoscience Center,Department of Applied Physics,A. Mathematics,U. Columbia,Department of Aerospace,M. Engineering,U. N. Dame +16 more
TL;DR: Defect coordinates were automatically extracted from defect detections maps showing that STM image analysis enhanced by machine learning can be used to dramatically increase sample characterization throughput.