The chemistry of ZnWO₄ nanoparticle formation

The need for a change away from classical nucleation and growth models for the description of nanoparticle formation is highlighted. By the use of in situ total X-ray scattering experiments the transformation of an aqueous polyoxometalate precursor mixture to crystalline ZnWO$_{4}$ nanoparticles under hydrothermal conditions was followed. The precursor solution is shown to consist of specific Tourné-type sandwich complexes. The formation of pristine ZnWO$_{4}$ within seconds is understood on the basis of local restructuring and three-dimensional reordering preceding the emergence of long range order in ZnWO$_{4}$ nanoparticles. An observed temperature dependent trend in defect concentration can be rationalized based on the proposed formation mechanism. Following nucleation the individual crystallites were found to grow into prolate morphology with elongation along the unit cell c-direction. Extensive electron microscopy characterization provided evidence for particle growth by oriented attachment; a notion supported by sudden particle size increases observed in the in situ total scattering experiments. A simple continuous hydrothermal flow method was devised to synthesize highly crystalline monoclinic zinc tungstate (ZnWO$_{4}$) nanoparticles in large scale in less than one minute. The present results highlight the profound influence of structural similarities in local structure between reactants and final materials in determining the specific nucleation of nanostructures and thus explains the potential success of a given synthesis procedure in producing nanocrystals. It demonstrates the need for abolishing outdated nucleation models, which ignore subtle yet highly important system dependent differences in the chemistry of the forming nanocrystals

Machine Learning for Analysis of Experimental Scattering and Spectroscopy Data in Materials Chemistry

The rapid growth of materials chemistry data, driven by advancements in large-scale radiation facilities as well as laboratory instruments, has outpaced conventional data analysis and modelling methods, which can require enormous manual effort. To address this bottleneck, we investigate the application of supervised and unsupervised machine learning (ML) techniques for scattering and spectroscopy data analysis in materials chemistry research. Our perspective focuses on ML applications in powder diffraction (PD), pair distribution function (PDF), small-angle scattering (SAS), inelastic neutron scattering (INS), and X-ray absorption spectroscopy (XAS) data, but the lessons that we learn are generally applicable across materials chemistry. We review the ability of ML to efficiently and accurately identify physical and structural models and extract information from experimental data. Furthermore, we discuss the challenges associated with supervised ML and highlight how unsupervised ML can mitigate these limitations, thus enhancing experimental materials chemistry data analysis. Our perspective emphasises the transformative potential of ML in materials chemistry characterisation and identifies promising directions for future applications. The perspective aims to guide newcomers to ML-based experimental data analysis, alerting them to the potential pitfalls and offering guidance for success

A GPU-Accelerated Open-Source Python Package for Calculating Powder Diffraction, Small-Angle-, and Total Scattering with the Debye Scattering Equation

The Debye scattering equation, derived in 1915 by Peter Debye, is used to calculate scattering intensities from atomic structures considering the position of each atom in the structure. The Debye scattering equation can be used to compute the scattering pattern of any atomic structure and is commonly used to study both crystalline and non-crystalline materials with a range of scattering techniques like powder diffraction (PD), total scattering (TS) with pair distribution function (PDF) analysis and small-angle scattering (SAS). Although the Debye scattering equation is extremely versatile, the computation of the double sum, which scales O(N^2), has limited the practical use of the equation. We introduce a GPU-accelerated open-source Python package, named DebyeCalculator, for rapid calculation of the Debye scattering equation from chemical structures represented as xyz-files or CIF-files. The xyz-format is commonly used in materials chemistry for the description of discrete particles and simply consists of a list of atomic identities and their respective Cartesian coordinates (x, y and z). DebyeCalculator can also take a crystallographic information file (CIF) and a user-defined spherical radius as input to generate an xyz-file from which a scattering pattern is calculated. DebyeCalculator is an open-source project (licensed under the Apache License 2.0) that is readily available through GitHub: https://github.com/FrederikLizakJohansen/DebyeCalculator and PyPi (https://pypi.org/project/DebyeCalculator/). It can also be run through an interactive interface, where users can simulate PD, TS, SAS and PDF data from structural models on both CPU and GPU

Elucidating the Steady-State OER Activity of (Ni1-xFex)OOH Binary Nanoparticles in As-prepared and Purified KOH Electrolyte Solutions

Replacing critical raw materials employed in water electrolysis applications as electrocatalysts with earth-abundant materials is paramount for the future upscaling to industrial dimensions. In that regard, Ni and Ni-based multimetallic hydroxides, above all NiFe-hydroxides, have shown promising performance towards the oxygen evolution reaction (OER) in alkaline conditions. However, it has been shown that the extraordinary performance of these materials is owed largely to Fe impurities found in commercial KOH from which electrolyte solutions are prepared. The mechanism of action of these impurities is still not fully understood, and therefore, at the heart of ongoing discussions. In this study, we investigate the OER activity of different nanostrcutured (Ni1-xFex)OOH samples and find their activities to be influenced differently by the presence of Fe impurities in the electrolyte. From the gathered data, we conclude that the presence of Fe impurities impacts gravely the structure sensitivity of the OER. In purified electrolyte solutions the OER appears to be a structure-sensitive reaction while this seems not to be the case in the presence of said impurities

A Reproducible and Scalable Method for Producing Fluorescent Polystyrene Nanoparticles

In this work polystyrene nanoparticles (PS NPs) were fabricated from an emulsion of PS/toluene in water using various surfactants, and purified via dialysis in a simple procedure. The synthesis process was carried out at room temperature, without hazardous chemicals, and with a workload of 5 hours. The investigation was performed to evaluate the limits for production of PS NPs with comparable properties. A robust PS NP synthesis procedure was developed, repeated, and tested by three independent researches. The procedure was up-scaled to prove the applicability of the method and the NPs were prepared with four different hydrophobic dyes. All products were found to be comparable, and it was concluded that the method reported here can provide PS NPs with or without dye dopants, and that it provides access to PS NPs with an average diameter of 25 nm in a reproducible size distribution

DeepStruc: Towards structure solution from pair distribution function data using deep generative models

Structure solution of nanostructured materials that have limited long-range remains a bottleneck in materials development. We present a deep learning algorithm, DeepStruc, that can solve a simple nanoparticle structure directly from a Pair Distribution Function obtained from total scattering data by using a conditional variational autoencoder (CVAE). We first apply DeepStruc to PDFs from seven different structure types of monometallic nanoparticles, and show that structures can be solved from both simulated and experimental PDFs, including PDFs from nanoparticles that are not present in the training distribution. We also apply DeepStruc to a system of hcp, fcc and stacking faulted nanoparticles, where DeepStruc recognizes stacking faulted nanoparticles as an interpolation between hcp and fcc nanoparticles and is able to solve stacking faulted structures from PDFs. Our findings suggests that DeepStruc is a step towards a general approach for structure solution of nanomaterials

Size-induced amorphous structure in tungsten oxide nanoparticles

The properties of functional materials are intrinsically linked to their atomic structure. When going to the nanoscale, size-induced structural changes in atomic structure often occur, however these are rarely well-understood. Here, we systematically investigate the atomic structure of tungsten oxide nanoparticles as a function of the nanoparticle size and observe drastic changes when the particles are smaller than 5 nm, where the particles are amorphous. The tungsten oxide nanoparticles are synthesized by thermal decomposition of ammonium metatungstate hydrate in oleylamine and by varying the ammonium metatungstate hydrate concentration, the nanoparticle size, shape and structure can be controlled. At low concentrations, nanoparticles with a diameter of 2-4 nm form and adopt an amorphous structure that locally resembles the structure of polyoxometalate clusters. When the concentration is increased the nanoparticles become elongated and form nanocrystalline rods up to 50 nm in length. The study thus reveals a size-dependent amorphous structure when going to the nanoscale and provides further knowledge on how metal oxide crystal structures changes at extreme length scales