A Comparative Analysis on LaueUtil and PRECOGNITION Software Packages as Tools in Treating the Small Molecule Time-Resolved Laue Diffraction Measurements at High Flux X-ray facilities

TL;DR

This study compares LaueUtil and PRECOGNITION software for analyzing time-resolved Laue diffraction data of metal organic systems, highlighting their strengths and limitations in data completeness and map quality at high flux X-ray facilities.

Contribution

It provides a systematic comparison of two software packages, demonstrating their effectiveness and challenges in processing time-resolved Laue crystallography data for metal organic complexes.

Findings

LaueUtil yields high reliability with low completeness and clean maps.

PRECOGNITION achieves higher completeness but with more noise.

Both methods have specific advantages and limitations in data treatment.

Abstract

Investigating metal organic systems with time-resolved photocrystallography poses a unique challenge while interpreting the time dependent photodifference maps. In these difference Fourier maps, the signals correspond to the movement of heavy metal atoms always overpower the signals from much lighter atoms attached to them. For a systematic assessment of the quality of the photodifference maps obtained from metal organic systems, in this work, LaueUtil and PRECOGNITION software were used to treat time-resolved Laue crystallography data of a [2x2] matrix-like Fe(II) complex. The rigid spot identification method in LaueUtil allows to identify and index > 250,000 reflections per 10 datasets. Though this leads to low completeness (< 30%), the software only treats the information from highly reliable diffraction spots. As a result, clean photodifference maps and small values in the thermal scale factor have been obtained. In the PRECOGNITION case, the package indexed more than 160,000 reflections per dataset. The resulting completeness is higher (>86%) and useful photodifference maps can be obtained by careful data treatment. However, the dependence on the refinement of the lambda curve as well as the degradation of the sample may be reflected on the large thermal scale factors which also contribute as noise in the photodifference maps.