Edge-Localized Strain in MoS<sub>2</sub> Nanobubbles Resolved at 5 nm by Chemical Imaging and Geometric Mechanics

ORAL

Abstract

Two-dimensional transition metal dichalcogenides (TMDs) exhibit remarkable optical and electronic tunability under nanoscale strain and defect engineering. In this work, we employ high-resolution tip-enhanced Raman spectroscopy (TERS) to spatially resolve vibrational and electronic responses from individual nanobubbles in monolayer MoS2 formed on plasmonic Au substrates. The confined geometry of the nanobubble induces localized strain and curvature, leading to pronounced bandgap modulation and enhanced Raman response. High-resolution TERS mapping reveals strong enhancement and strain-induced frequency shifts with 5 nm spatial precision at cryogenic temperature (78 K). Correlated topographic and spectroscopic imaging enables direct visualization of the interplay between local strain gradients, sulfur vacancy distributions, and near-field plasmonic enhancement. We verified a maximum tensile strain of ∽1.15–1.34% at the nanobubble edge, which gradually diminishes toward the center, yielding a cross-sectional strain profile consistent with a doughnut-shaped distribution. Additionally, to compare with the experimentally estimated strain, we provided geometric mechanistic methods to investigate the average strain of MoS2 nanobubble. These findings provide new insight into nanoscale structure–property relationships in strained 2D materials and highlight the power of TERS for investigating quantum-confined defects and excitonic phenomena in low-dimensional systems.

*Work performed at the Center for Nanoscale Materials, a U.S. Department of Energy Office of Science User Facility, was supported by the U.S. DOE, Office of Basic Energy Sciences, under Contract No. DE-AC02-06CH11357. Additionally, support from National Science Foundation (NSF) DMR-2211474. Primary funding for synthesis work comes from the Air Force Office of Scientific Research (FA9550-21-1-0323). Theoretical part supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Data, Artificial Intelligence, and Machine Learning at DOE Scientific User Facilities program for support under Award Number 34532 (Digital Twins).

Presenters

  • Sayantan Mahapatra

    • Argonne National Laboratory

Authors

  • Sayantan Mahapatra

    • Argonne National Laboratory
  • Soumyajit Rajak

    • University of Illinois Chicago
  • Sukriti Manna

    • Argonne National Laboratory
  • Tomojit Chowdhury

    • University of Chicago
  • Nathan P Guisinger

    • Argonne National Laboratory
  • Nan Jiang

    • University of Illinois at Chicago
  • Jeffrey Randall Guest

    • Argonne National Laboratory