Using Polarized Spectroscopy to Investigate Order in Thin-Films of Ionic Self-Assembled Materials Based on Azo-Dyes

Three series of ionic self-assembled materials based on anionic azo-dyes and cationic benzalkonium surfactants were synthesized and thin films were prepared by spin-casting. These thin films appear isotropic when investigated with polarized optical microscopy, although they are highly anisotropic. Here, three series of homologous materials were studied to rationalize this observation. Investigating thin films of ordered molecular materials relies to a large extent on advanced experimental methods and large research infrastructure. A statement that in particular is true for thin films with nanoscopic order, where X-ray reflectometry, X-ray and neutron scattering, electron microscopy and atom force microscopy (AFM) has to be used to elucidate film morphology and the underlying molecular structure. Here, the thin films were investigated using AFM, optical microscopy and polarized absorption spectroscopy. It was shown that by using numerical method for treating the polarized absorption spectroscopy data, the molecular structure can be elucidated. Further, it was shown that polarized optical spectroscopy is a general tool that allows determination of the molecular order in thin films. Finally, it was found that full control of thermal history and rigorous control of the ionic self-assembly conditions are required to reproducibly make these materials of high nanoscopic order. Similarly, the conditions for spin-casting are shown to be determining for the overall thin film morphology, while molecular order is maintained

POMFinder: Identifying polyoxometalate cluster structures from pair distribution function data using explainable machine learning

Characterisation of material structure with Pair Distribution Function (PDF) analysis typically involves refining a structure model against an experimental dataset. However, finding or constructing a suitable atomic model for PDF modelling can be an extremely labour-intensive task, requiring carefully browsing through large numbers of possible models. We present POMFinder, a machine learning (ML) classifier that rapidly screens a database of structures, here polyoxometalate (POM) clusters, to identify candidate structures for PDF data modelling. The approach is demonstrated to identify suitable POMs on experimental data, including in situ data collected with fast acquisition time. This automated approach shows significant potential for identifying suitable structure models for structure refinements to extract quantitative, structural parameters in materials chemistry research. The code is open source and user-friendly, making it accessible to those without prior ML knowledge. We also demonstrate that POMFinder offers a promising modelling framework for combined modelling of multiple scattering techniques compared to conventional refinement methods

Characterising the Atomic Structure of Mono-Metallic Nanoparticles from X-Ray Scattering Data Using Conditional Generative Models

The development of new nanomaterials for energy technologies is dependent on understanding the intricate relation between material properties and atomic structure. It is, therefore, crucial to be able to routinely characterise the atomic structure in nanomaterials, and a promising method for this task is Pair Distribution Function (PDF) analysis. The PDF can be obtained through Fourier transformation of x-ray total scattering data, and represents a histogram of all interatomic distances in the sample. Going from the distance information in the PDF to a chemical structure is an unassigned distance geometry problem (uDGP), and solving this is often the bottleneck in nanostructure analysis. In this work, we propose to use a Conditional Variational Autoencoder (CVAE) to automatically solve the uDGP to obtain valid chemical structures from PDFs. We use a simple model system of hypothetical mono-metallic nanoparticles containing up to 100 atoms in the face centered cubic (FCC) structure as a proof of concept. The model is trained to predict the assigned distance matrix (aDM) from a simulated PDF of the structure as the conditional input. We introduce a novel representation of structures by projecting them inside a unit sphere and adding additional anchor points or satellites to help in the reconstruction of the chemical structure. The performance of the CVAE model is compared to a Deterministic Autoencoder (DAE) showing that both models are able to solve the uDGP reasonably well. We further show that the CVAE learns a structured and meaningful latent embedding space which can be used to predict new chemical structures

Atomic structural changes in the formation of transition metal tungstates: The role of polyoxometalate structures in material crystallization

Nucleation processes in wet-chemical synthesis methods are poorly understood, nevertheless an atomistic understanding of material formation would aid in the design of synthesis methods for tailor-made functional materials. Here, in situ X-ray total scattering experiments were performed during the hydrothermal synthesis of wolframite-type MWO4 (M: Mn, Fe, Co, Ni), enabling pair distribution function (PDF) analysis of the process. Upon mixing of the aqueous precursors, a crystalline precursor formed for the MnWO4 synthesis, while amorphous pastes formed for the FeWO4, CoWO4 and NiWO4 syntheses. Upon heating, the crystalline MnWO4 precursor converted directly to a crystalline wolframite-type MnWO4 phase, while the amorphous precursor led to the formation of an intermediate phase before the crystalline tungstates. The structure of the amorphous precursors was studied in detail using PDF analysis. Database mining was initially used to extract chemically relevant cluster structures, with the conclusion that the structure of the precursor contains Keggin fragments, well known from polyoxometalate chemistry. Such fragments are present in the Tourné ‘sandwich’ cluster, which has previously been found to be involved in the formation of some tungstates. We then used our recently developed ML-MotEx algorithm to identify which structural motifs in the sandwich structure are important to obtain a good fit of the data throughout the reaction. This analysis led to the identification of a skewed sandwich cluster to best describe the amorphous precursor structures. For the intermediate phase, ML-MotEx favoured motifs found both in the precursor and product phases, and the PDF of the intermediate phase could be described up to 20 Å by a disordered MWO4 structure. We found that the more disordered the precursor phase is, the longer reaction time is required to form crystalline products. More generally, we see that polyoxometalate chemistry is useful when describing the initial wet-chemical formation of mixed metal oxides

Influence of precursor structure on the formation of tungsten oxide polymorphs

Understanding material nucleation processes is crucial for the development of synthesis pathways for tailormade materials. However, we currently have little knowledge of the influence of the precursor solution structure on the formation pathway of materials. We here use in situ total scattering to show how the precursor solution structure influences which crystal structure is formed during the hydrothermal synthesis of tungsten oxides. We investigate the synthesis of tungsten oxide from the two polyoxometalate salts, ammonium metatungstate and ammonium paratungstate. In both cases, a hexagonal ammonium tungsten bronze (NH4)0.25WO3, is formed as the final product. If the precursor solution contains metatungstate clusters, this phase forms directly in the hydrothermal synthesis. However, if the paratungstate B cluster is present at the time of crystallization, a metastable intermediate phase in the form of a pyrochlore-type tungsten oxide, WO30*5H2O, initially forms. The pyrochlore structure then undergoes a phase transformation into the tungsten bronze phase. Our studies thus experimentally show that the precursor cluster structure present at the moment of crystallization directly influences the formed crystalline phase and suggest that the precursor structure just prior to crystallization can be used as a tool for targeting specific crystalline phases of interest

Structural Changes during the Growth of Atomically Precise Metal Oxido Nanoclusters from Combined Pair Distribution Function and Small‐Angle X‐ray Scattering Analysis

The combination of in situ pair distribution function (PDF) analysis and small-angle X-ray scattering (SAXS) enables analysis of the formation mechanism of metal oxido nanoclusters and cluster–solvent interactions as they take place. Herein, we demonstrate the method for the formation of clusters with a [Bi38O45] core. Upon dissolution of crystalline [Bi$_6$O$_5$(OH)$_3$(NO$_3$)$_5$]⋅3 H$_2$O in DMSO, an intermediate rapidly forms, which slowly grows to stable [Bi$_{38}$O$_{45}$] clusters. To identify the intermediate, we developed an automated modeling method, where smaller [BixOy] structures based on the [Bi$_{38}$O$_{45}$] framework are tested against the data. [Bi$_{22}$O$_{26}$] was identified as the main intermediate species, illustrating how combined PDF and SAXS analysis is a powerful tool to gain insight into nucleation on an atomic scale. PDF also provides information on the interaction between nanoclusters and solvent, which is shown to depend on the nature of the ligands on the cluster surface

Structural Changes during the Growth of Atomically Precise Metal Oxido Nanoclusters from Combined Pair Distribution Function and Small‐Angle X‐ray Scattering Analysis

The combination of in situ pair distribution function (PDF) analysis and small-angle X-ray scattering (SAXS) enables analysis of the formation mechanism of metal oxido nanoclusters and cluster–solvent interactions as they take place. Herein, we demonstrate the method for the formation of clusters with a [Bi38O45] core. Upon dissolution of crystalline [Bi$_6$O$_5$(OH)$_3$(NO$_3$)$_5$]⋅3 H$_2$O in DMSO, an intermediate rapidly forms, which slowly grows to stable [Bi$_{38}$O$_{45}$] clusters. To identify the intermediate, we developed an automated modeling method, where smaller [BixOy] structures based on the [Bi$_{38}$O$_{45}$] framework are tested against the data. [Bi$_{22}$O$_{26}$] was identified as the main intermediate species, illustrating how combined PDF and SAXS analysis is a powerful tool to gain insight into nucleation on an atomic scale. PDF also provides information on the interaction between nanoclusters and solvent, which is shown to depend on the nature of the ligands on the cluster surface