The direct observation of the structure of micrometer-sized vapor-deposited ice is performed at Pohang Accelerator Laboratory x-ray free electron laser (PAL-XFEL). The formation of micrometer-sized ice crystals and their structure is important in various fields, including atmospheric science, cryobiology, and astrophysics, but understanding the structure of micrometer-sized ice crystals remains challenging due to the lack of direct observation. Using intense x-ray diffraction from PAL-XFEL, we could observe the structure of micrometer-sized vapor-deposited ice below 150 K with a thickness of 2–50 μm grown in an ultrahigh vacuum chamber. The structure of the ice grown comprises cubic and hexagonal sequences that are randomly arranged to produce a stacking-disordered ice. We observed that ice with a high cubicity of more than 80% was transformed to partially oriented hexagonal ice when the thickness of the ice deposition grew beyond 5 μm. This suggests that precise temperature control and clean deposition conditions allow μm-thick ice films with high cubicity to be grown on hydrophilic Si3N4 membranes. The low influence of impurities could enable in situ diffraction experiments of ice nucleation and growth from interfacial layers to bulk ice.

Ice crystallization in supercooled water is a complex phenomenon with far-reaching implications across scientific disciplines, including cloud formation physics and cryopreservation. Experimentally studying such complexity can be a highly data-driven and data-hungry endeavor because of the need to record rare events that cannot be triggered on demand. Here, we describe such an experiment comprising 561 million images of X-ray free-electron laser (XFEL) diffraction patterns (2.3 $\mathrm{PB}$ raw data) spanning the disorder-to-order transition in micrometer-sized supercooled water droplets. To effectively analyze these patterns, we propose a data reduction (i.e., coarse-graining) and dimensionality reduction (i.e., principal component analysis) strategy. We show that a simple set of criteria on this reduced dataset can efficiently classify these patterns in the absence of reference diffraction signatures, which we validated using more precise but computationally expensive unsupervised machine learning techniques. For hit-finding, our strategy attained 98% agreement with our cross-validation. We speculate that these strategies may be generalized to other types of large high-dimensional datasets generated at high-throughput XFEL facilities.

We present the first experimental characterization, using GHz to THz dielectric relaxation spectroscopy, of the collective rearrangement of the hydrogen bond network of water molecules nanoscopically confined in phospholipid multilamellar vesicles. We determined a distinct relaxation peak at ~40 GHz and the Kirkwood correlation factor of approximately 1.2, which are the indication of acceleration of the collective reorientation dynamics with nanoconfinement, and strong disruption of the hydrogen bond network compared with that of bulk water. Combined with X-ray observation, it has estimated that ~70% of the nanoconfined water contributes to the accelerated relaxation mode.

We present a novel approach to determine the hydration number of aqueous electrolytes by means of ab initio molecular dynamics. An hydration status analysis is devised to quantify the cooperative effect of ions on the reorientational dynamics of different water subpopulations in electrolyte solutions. The methodology is applied to predict the hydration numbers, h, as the number moles of water molecules per mole of dissolved salt that no longer participate in bulk-like reorientational dynamics. The obtained hydration number of MgCl 2 (h = 15) is consistent with THz-DR experiment, measuring reorientational dynamics of water in solutions. By providing a link with the micro-scale dynamical behaviour of ions and water molecules, this approach represents a generally applicable, welldefined methodology to quantify hydration numbers of ions and molecules in aqueous solutions.