Quantitative nanoparticle structures from ultrafast electron crystallography data

TL;DR

This paper demonstrates that ultrafast electron crystallography data can be quantitatively refined to determine nanoparticle structures, establishing a link with traditional diffraction analysis and enabling reliable analysis of dynamic nanoparticle states.

Contribution

It introduces a method to refine nanoparticle structures from UEC data using PDF techniques, bridging UEC and x-ray/neutron diffraction analysis.

Findings

UEC data can differentiate nanoparticle shapes.

Refined atomic positions are robust to systematic errors.

Established equivalence between UEC and PDF analysis.

Abstract

We describe the quantitative refinement of nanoparticle structures from gold nanoparticles probed by ultrafast electron crystallography (UEC). We establish the equivalence between the modified radial distribution function employed in UEC and the atomic pair distribution function (PDF) used in x-ray and neutron powder diffraction analysis. By leveraging PDF refinement techniques, we demonstrate that UEC data are of sufficient quality to differentiate between cuboctahedral, decahedral and icosahedral nanoparticle models. Furthermore, we identify the signatures of systematic errors that may occur during data reduction and show that atomic positions refined from UEC are robust to these errors. This work serves as a foundation for reliable quantitative structural analysis of time-resolved laser-excited nanoparticle states.