In Situ Studies of the Formation of Tungsten and Niobium Oxide Nanoparticles: Towards Automated Analysis of Reaction Pathways from PDF Analysis using the Pearson Correlation Coefficient

Abstract

Using Pair Distribution Function (PDF) analysis of in situ total scattering data, we investigate the formation of tungsten and niobium oxides in a simple solvothermal synthesis. We use Pearson Correlation Coefficient (PCC) analysis of the time resolved PDFs to both map the structural changes taking place throughout the synthesis and identify structural models for precursor and product through PCC-based structure mining. Our analysis first shows that ultra-small tungsten and niobium oxide nanoparticles form instantaneously upon heating, with sizes between 1.5 and 2 nm. We show that the main structural motifs in the nanoparticles can be described with structures containing pentagonal columns, which is characteristic for many bulk tungsten and niobium oxides. We furthermore elucidate the structure of the precursor complex as clusters of octahedra with O-and Cl-ligands. The PCC based methodology automates the structure characterization and proves useful for analysis of large datasets of for example, time resolved X-ray scattering studies. The PCC is implemented in 'PDF in the cloud', a web platform for PDF analysis.

Fundamentals, status and challenges of direct recycling technologies for lithium ion batteries

“…Kjær et al used in situ PDF to investigate the formation of tungsten and niobium oxides in a simple solvothermal synthesis, thus revealing the specific reaction and change process hidden in this black box. 283 Coincidentally, Zhang et al characterized the synthesis process of high-nickel layered oxide materials by synchrotron radiation X-ray total scattering, wherein temperature-resolved PDF patterns were obtained via Fourier transform, as illustrated in Fig. 22d.…”

Exploration of anion effects in solvothermal synthesis using in situ X-ray diffraction

“…Most studies have been focused on a single chemical system/product with exploration of select parts of the relevant parameter space. Thorough work has been presented on the structure-directing effect of additives in synthesis of CuPd nanoparticles, 24 BaTiO 3 , 25,26 AgCuO 2 , 27 YMnO 3 , 28 and NaNbO 3 , 29 TiO 2 growth control with and without surfactants, 30,31 product dependence on reaction temperature, time, precursor water content and pH in synthesis of In and Ga based materials, 32,33 complex stoichiometry/coordination of W and Nb oxides, 34 and phase control, crystallite morphology, magnetic properties of SrFe 12 O 19 , 35 and the formation mechanism and attainable stoichiometry of high entropy alloy nanoparticles. 36–39…”

Machine learning for analysis of experimental scattering and spectroscopy data in materials chemistry

“…3–5 It is now common to acquire large amounts of data from e.g. time-resolved in situ or operando experiments, 6–8 and the combination of scattering and spectroscopy with computed tomography, allowing detailed position-resolved studies 9–12 also results in large datasets. To extract information from the data, careful analysis is needed.…”