Simulating STEM imaging of nanoparticles in micrometers-thick substrates.

Area of Science:

• Materials Science
• Electron Microscopy
• Computational Physics

Background:

• Scanning transmission electron microscopy (STEM) is vital for nanoscale imaging.
• Simulating STEM imaging aids in understanding complex sample interactions and optimizing imaging parameters.
• Accurate simulation requires incorporating realistic electron source characteristics and beam dynamics.

Purpose of the Study:

• To simulate STEM imaging of 3D samples, including gold nanoparticles on substrates.
• To investigate the influence of electron dose and beam broadening on image quality and resolution.
• To validate the simulation method by comparing it with experimental data.

Main Methods:

• Utilized Monte Carlo simulations with adapted CASINO software for STEM imaging.
• Incorporated electron source noise, conical beam shape, and 3D scanning.
• Simulated focal series and compared results with experimental data for nanoparticles on carbon and water substrates.

Main Results:

• Electron dose significantly affects nanoparticle visibility in simulated STEM images.
• Beam broadening degrades spatial resolution and signal-to-noise ratio in 3D datasets.
• Monte Carlo simulations closely matched experimental STEM images and signal-to-noise levels.

Conclusions:

• The developed Monte Carlo simulation strategy is effective for calculating STEM images of arbitrary geometries and amorphous compositions.
• This method can optimize STEM microscope settings, especially for low-dose imaging.
• The simulations are valuable for equipment design and specimen composition analysis.