Visualized SERS Imaging of Single Molecule by Ag/Black Phosphorus Nanosheets

Nanomicro Lett. 2022 Mar 15;14(1):75. doi: 10.1007/s40820-022-00803-x.

Abstract

  1. Ag/BP-NS exhibit remarkable surface-enhanced Raman scattering performance with single-molecule detection ability. This remarkable enhancement can be attributed to the synergistic resonance enhancement of R6G molecular resonance, photo-induced charge transfer resonance and electromagnetic resonance.

  2. A new polarization-mapping method was proposed, which can quickly screen out single-molecule signals and prove that the obtained spectra are emitted by single molecule.

  3. The recognition of different tumor exosomes can be realized combining the method of machine learning.

Abstract: Single-molecule detection and imaging are of great value in chemical analysis, biomarker identification and other trace detection fields. However, the localization and visualization of single molecule are still quite a challenge. Here, we report a special-engineered nanostructure of Ag nanoparticles embedded in multi-layer black phosphorus nanosheets (Ag/BP-NS) synthesized by a unique photoreduction method as a surface-enhanced Raman scattering (SERS) sensor. Such a SERS substrate features the lowest detection limit of 10–20 mol L−1 for R6G, which is due to the three synergistic resonance enhancement of molecular resonance, photo-induced charge transfer resonance and electromagnetic resonance. We propose a polarization-mapping strategy to realize the detection and visualization of single molecule. In addition, combined with machine learning, Ag/BP-NS substrates are capable of recognition of different tumor exosomes, which is meaningful for monitoring and early warning of the cancer. This work provides a reliable strategy for the detection of single molecule and a potential candidate for the practical bio-application of SERS technology.

Supplementary Information: The online version contains supplementary material available at 10.1007/s40820-022-00803-x.

Keywords: Black phosphorus; Exosome; Machine learning; SERS; Single molecule.