Toward time-resolved laser T-jump/X-ray probe spectroscopy in aqueous solutions

Vibrational Coherences of the Photoinduced Mixed-Valent Creutz-Taube Ion Revealed by Excited State Dynamics

A recent study of photoinduced mixed-valency in the one-electron reduced form (μ-pz)[RuII(NH3)5]24+ of the Creutz-Taube ion used transient absorption spectroscopy with vis-NIR broadband detection to uncover a mixed-valent excited state with a typical intervalence charge transfer band and a nanosecond lifetime [Pieslinger et al. Angew. Chem., Int. Ed. 2022, 61, e202211747]. Herein, we use excited state dynamics simulations with implicit solvation to elucidate the electronic and vibrational evolution in the first 10 ps after the optical excitation. A manifold of excited states with weak interaction between the metal centers is populated already at time zero due to the breakdown of the Condon approximation and dominates the population of electronic states at short time scales (<0.5 ps). A long-lived vibrational wave packet mostly confined to oscillations of the metal center-bridge distances is observed. The oscillations are traced to the electronic structure properties of states with weak metal-metal coupling. The long-lived mixed-valent excited state of the Creutz-Taube ion analogue is formed vibrationally cold and has a more compact geometry. While experimentally, intersystem crossing and vibrational relaxation were deduced to be completed within 1 ps, our analysis indicates that both processes might persist at longer times.

Engraving of stainless-steel wires to improve optical quality of closed-loop wire-guided flow jet systems for optical and X-ray spectroscopy

This paper describes performance enhancement developments to a closed-loop pump-driven wire-guided flow jet (WGJ) for ultrafast X-ray spectroscopy of liquid samples. Achievements include dramatically improved sample surface quality and reduced equipment footprint from 7 × 20 cm² to 6 × 6 cm², cost, and manufacturing time. Qualitative and quantitative measurements show that micro-scale wire surface modification yields significant improvements to the topography of the sample liquid surface. By manipulating their wettability, it is possible to better control the liquid sheet thickness and to obtain a smooth liquid sample surface, as demonstrated in this work.

Accurate, affordable, and generalizable machine learning simulations of transition metal x-ray absorption spectra using the XANESNET deep neural network

The affordable, accurate, and generalizable prediction of spectroscopic observables plays a key role in the analysis of increasingly complex experiments. In this article, we develop and deploy a deep neural network-XANESNET-for predicting the lineshape of first-row transition metal K-edge x-ray absorption near-edge structure (XANES) spectra. XANESNET predicts the spectral intensities using only information about the local coordination geometry of the transition metal complexes encoded in a feature vector of weighted atom-centered symmetry functions. We address in detail the calibration of the feature vector for the particularities of the problem at hand, and we explore the individual feature importance to reveal the physical insight that XANESNET obtains at the Fe K-edge. XANESNET relies on only a few judiciously selected features-radial information on the first and second coordination shells suffices along with angular information sufficient to separate satisfactorily key coordination geometries. The feature importance is found to reflect the XANES spectral window under consideration and is consistent with the expected underlying physics. We subsequently apply XANESNET at nine first-row transition metal (Ti-Zn) K-edges. It can be optimized in as little as a minute, predicts instantaneously, and provides K-edge XANES spectra with an average accuracy of ∼±2%-4% in which the positions of prominent peaks are matched with a >90% hit rate to sub-eV (∼0.8 eV) error.

First events in the coil-to-globule transition of PVME in water: An ultrafast temperature jump - time-resolved elastic light scattering study

HYPOTHESIS

The coil-to-globule transition is an essential phenomenon in protein and polymer solutions. Late stages of such transitions, >1 µs, have been thoroughly studied. Yet, the initial ones are a matter of speculations. Here, we present the first observation of a sub-nanosecond stage of the coil-to-globule transition of poly (vinyl methyl ether), PVME, in water.

EXPERIMENTS

The detection of an early stage of the coil-to-globule transition has been possible thanks to a novel experimental approach - time-resolved elastic light scattering study, following an ultrafast temperature jump. We identified a molecular process active in the observed stage of the transition with use of broadband dielectric spectroscopy.

FINDINGS

In the experiment's time window, from a few ps to around 600 ps, we observed an increase in the light scattering intensity 300-400 ps after the temperature jump that heated the sample above its lower critical solution temperature (LCST). The observed time coincides with the time of segmental relaxation of PVME, determined by broadband dielectric spectroscopy in the temperature range of the LCST of the PVME/water mixture. This coincidence strongly suggests that the observed herein stage of coil-to-globule transition is the rapid formation of local nuclei along the polymer chain. Those nuclei may grow and aggregate in later stages of the process, which are out of our experimental time window.

Nanoporous TiN/TiO2/Alumina Membrane for Photoelectrochemical Hydrogen Production from Sewage Water

An aluminum oxide, Al2O3, template is prepared using a novel Ni imprinting method with high hexagonal pore accuracy and order. The pore diameter after the widening process is about 320 nm. TiO2 layer is deposited inside the template using atomic layer deposition (ALD) followed by the deposition of 6 nm TiN thin film over the TiO2 using a direct current (DC) sputtering unit. The prepared nanotubular TiN/TiO2/Al2O3 was fully characterized using different analytical tools such as X-ray diffraction (XRD), Energy-dispersive X-ray (EDX) spectroscopy, scanning electron microscopy (SEM), and optical UV-Vis spectroscopy. Exploring the current-voltage relationships under different light intensities, wavelengths, and temperatures was used to investigate the electrode's application before and after Au coating for H2 production from sewage water splitting without the use of any sacrificing agents. All thermodynamic parameters were determined, as well as quantum efficiency (QE) and incident photon to current conversion efficiency (IPCE). The QE was 0.25% and 0.34% at 400 mW·cm-2 for the photoelectrode before and after Au coating, respectively. Also, the activation energy was 27.22 and 18.84 kJ·mol-1, the enthalpy was 24.26 and 15.77 J·mol-1, and the entropy was 238.1 and 211.5 kJ-1·mol-1 before and after Au coating, respectively. Because of its high stability and low cost, the prepared photoelectrode may be suitable for industrial applications.

Enhancing the analysis of disorder in X-ray absorption spectra: application of deep neural networks to T-jump-X-ray probe experiments

Many chemical and biological reactions, including ligand exchange processes, require thermal energy for the reactants to overcome a transition barrier and reach the product state. Temperature-jump (T-jump) spectroscopy uses a near-infrared (NIR) pulse to rapidly heat a sample, offering an approach for triggering these processes and directly accessing thermally-activated pathways. However, thermal activation inherently increases the disorder of the system under study and, as a consequence, can make quantitative interpretations of structural changes challenging. In this Article, we optimise a deep neural network (DNN) for the instantaneous prediction of Co K-edge X-ray absorption near-edge structure (XANES) spectra. We apply our DNN to analyse T-jump pump/X-ray probe data pertaining to the ligand exchange processes and solvation dynamics of Co2+ in chlorinated aqueous solution. Our analysis is greatly facilitated by machine learning, as our DNN is able to predict quickly and cost-effectively the XANES spectra of thousands of geometric configurations sampled from ab initio molecular dynamics (MD) using nothing more than the local geometric environment around the X-ray absorption site. We identify directly the structural changes following the T-jump, which are dominated by sample heating and a commensurate increase in the Debye-Waller factor.

The Role of Structural Representation in the Performance of a Deep Neural Network for X-Ray Spectroscopy

An important consideration when developing a deep neural network (DNN) for the prediction of molecular properties is the representation of the chemical space. Herein we explore the effect of the representation on the performance of our DNN engineered to predict Fe K-edge X-ray absorption near-edge structure (XANES) spectra, and address the question: How important is the choice of representation for the local environment around an arbitrary Fe absorption site? Using two popular representations of chemical space-the Coulomb matrix (CM) and pair-distribution/radial distribution curve (RDC)-we investigate the effect that the choice of representation has on the performance of our DNN. While CM and RDC featurisation are demonstrably robust descriptors, it is possible to obtain a smaller mean squared error (MSE) between the target and estimated XANES spectra when using RDC featurisation, and converge to this state a) faster and b) using fewer data samples. This is advantageous for future extension of our DNN to other X-ray absorption edges, and for reoptimisation of our DNN to reproduce results from higher levels of theory. In the latter case, dataset sizes will be limited more strongly by the resource-intensive nature of the underlying theoretical calculations.