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Combining quantitative ADF STEM with SiNx membrane-based MEMS devices: A simulation study with Pt nanoparticles.

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Area of Science:

  • Materials Science
  • Nanotechnology
  • Electron Microscopy

Background:

  • Accurate quantification of nanoparticles is crucial for materials characterization.
  • Scanning Transmission Electron Microscopy (STEM) with Annular Dark-Field (ADF) imaging is a powerful tool for atomic-scale analysis.
  • The use of thin membranes, such as silicon nitride (SiNx), is common for supporting nanoparticles during imaging.

Purpose of the Study:

  • To investigate the impact of a 20-nm-thick SiNx membrane on the quantitative accuracy of atomic-resolution ADF-STEM imaging.
  • To evaluate how various factors influence the precision of counting Platinum (Pt) atoms in nanoparticle columns.
  • To determine the optimal imaging conditions for reliable Pt nanoparticle quantification through SiNx membranes.

Main Methods:

  • Utilized computer simulations to model ADF-STEM imaging of Pt nanoparticles on SiNx membranes.
  • Analyzed the effects of nanoparticle/membrane arrangement, accelerating voltage, and nanoparticle thickness.
  • Incorporated the influence of adjacent atomic columns on atom counting accuracy.
  • Employed ADF scattering cross-sections for quantitative analysis.

Main Results:

  • Achieved an atom counting accuracy better than a single atom at 200 and 300 kV accelerating voltages.
  • At 80 kV, strong scattering from the SiNx membrane reduced atom counting accuracy to +/- 2 atoms.
  • Demonstrated that nanoparticle/membrane arrangement and adjacent atomic columns affect quantification accuracy.

Conclusions:

  • The SiNx membrane significantly influences quantitative ADF-STEM analysis of Pt nanoparticles.
  • Higher accelerating voltages (200-300 kV) enable high-accuracy atom counting, achieving sub-atomic precision.
  • Lower accelerating voltages (e.g., 80 kV) lead to reduced accuracy due to membrane scattering, necessitating careful consideration for quantitative studies.