Hafnia, HfO2, which is a wide band gap semiconducting oxide, is much less studied than the chemically similar zirconia (ZrO2). Here, we study the formation of hafnia nanocrystals from hafnium...

The structures of metal ions in solution constitute essential information for obtaining chemical insight spanning from catalytic reaction mechanisms to formation of functional nanomaterials. Here, we explore Zr4+ solution structures...

Understanding the nucleation and growth mechanisms of nanocrystals under hydro- and solvothermal conditions is key to tailoring functional nanomaterials. High-energy and high-flux synchrotron radiation is ideal for characterization by powder X-ray diffraction and X-ray total scattering in real time. Different versions of batch-type cell reactors have been employed in this work, exploiting the robustness of polyimide-coated fused quartz tubes with an inner diameter of 0.7 mm, as they can withstand pressures up to 250 bar and temperatures up to 723 K for several hours. Reported here are recent developments of the in situ setups available for general users on the P21.1 beamline at PETRA III and the DanMAX beamline at MAX IV to study nucleation and growth phenomena in solvothermal synthesis. It is shown that data suitable for both reciprocal-space Rietveld refinement and direct-space pair distribution function refinement can be obtained on a timescale of 4 ms.

Gallium oxides are of broad interest due to their wide band gaps and attractive photoelectric properties. Typically, the synthesis of gallium oxide nanoparticles is based on a combination of solvent-based...

Determination of crystal structures of nanocrystalline or amorphous compounds is a great challenge in solid‐state chemistry and physics. Pair distribution function (PDF) analysis of X‐ray or neutron total scattering data has proven to be a key element in tackling this challenge. However, in most cases, a reliable structural motif is needed as a starting configuration for structure refinements. Here, an algorithm that is able to determine the crystal structure of an unknown compound by means of an on‐the‐fly trained machine learning model, which combines density functional theory calculations with comparison of calculated and measured PDFs for global optimization in an artificial landscape, is presented. Due to the nature of this landscape, even metastable configurations and stacking disorders can be identified.