Ultrafast photoelectron spectroscopy of photoexcited aqueous ferrioxalate

The photochemistry of metal-organic compounds in solution is determined by both intra- and inter-molecular relaxation processes after photoexcitation. Understanding its prime mechanisms is crucial to optimise the reactive paths and control their outcome. Here we investigate the photoinduced dynamics of aqueous ferrioxalate ([Fe^III(C₂O₄)₃]^3-) upon 263 nm excitation using ultrafast liquid phase photoelectron spectroscopy (PES). The initial step is found to be a ligand-to-metal electron transfer, occuring on a time scale faster than our time resolution (≲30 fs). Furthermore, we observe that about 25% of the initially formed ferrous species population are lost in ∼2 ps. Cast in the contest of previous ultrafast infrared and X-ray spectroscopic studies, we suggest that upon prompt photoreduction of the metal centre, the excited molecules dissociate in <140 fs into the pair of CO₂ and [(CO₂)Fe^II(C₂O₄)₂]^3- fragments, with unity quantum yield. About 25% of these pairs geminately recombine in ∼2 ps, due to interaction with the solvent molecules, reforming the ground state of the parent ferric molecule.

Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning

Free-electron lasers providing ultra-short high-brightness pulses of X-ray radiation have great potential for a wide impact on science, and are a critical element for unravelling the structural dynamics of matter. To fully harness this potential, we must accurately know the X-ray properties: intensity, spectrum and temporal profile. Owing to the inherent fluctuations in free-electron lasers, this mandates a full characterization of the properties for each and every pulse. While diagnostics of these properties exist, they are often invasive and many cannot operate at a high-repetition rate. Here, we present a technique for circumventing this limitation. Employing a machine learning strategy, we can accurately predict X-ray properties for every shot using only parameters that are easily recorded at high-repetition rate, by training a model on a small set of fully diagnosed pulses. This opens the door to fully realizing the promise of next-generation high-repetition rate X-ray lasers.