MLstructureMining: a machine learning tool for structure identification from X-ray pair distribution functions.

Synchrotron X-ray techniques are essential for studies of the intrinsic relationship between synthesis, structure, and properties of materials. Modern synchrotrons can produce up to 1 petabyte of data per day. Such amounts of data can speed up materials development, but also comes with a staggering growth in workload, as the data generated must be stored and analyzed. We present an approach for quickly identifying an atomic structure model from pair distribution function (PDF) data from (nano)crystalline materials. Our model, MLstructureMining, uses a tree-based machine learning (ML) classifier. MLstructureMining has been trained to classify chemical structures from a PDF and gives a top-3 accuracy of 99% on simulated PDFs not seen during training, with a total of 6062 possible classes. We also demonstrate that MLstructureMining can identify the chemical structure from experimental PDFs from nanoparticles of CoFe2O4 and CeO2, and we show how it can be used to treat an in situ PDF series collected during Bi2Fe4O9 formation. Additionally, we show how MLstructureMining can be used in combination with the well-known methods, principal component analysis (PCA) and non-negative matrix factorization (NMF) to analyze data from in situ experiments. MLstructureMining thus allows for real-time structure characterization by screening vast quantities of crystallographic information files in seconds.

Population incidence and associated mortality of urinary tract infection in people living with dementia.

OBJECTIVES: Urinary tract infections (UTIs) frequently cause hospitalisation and death in people living with dementia (PLWD). We examine UTI incidence and associated mortality among PLWD relative to matched controls and people with diabetes and investigate whether delayed or withheld treatment further impacts mortality. METHODS: Data were extracted for n = 2,449,814 people aged ≥ 50 in Wales from 2000-2021, with groups matched by age, sex, and multimorbidity. Poisson regression was used to estimate incidences of UTI and mortality. Cox regression was used to study the effects of treatment timing. RESULTS: UTIs in dementia (HR=2.18, 95 %CI [1.88-2.53], p < .0) and diabetes (1.21[1.01-1.45], p = .035) were associated with high mortality, with the highest risk in individuals with diabetes and dementia (both) (2.83[2.40-3.34], p < .0) compared to matched individuals with neither dementia nor diabetes. 5.4 % of untreated PLWD died within 60 days of GP diagnosis-increasing to 5.9 % in PLWD with diabetes. CONCLUSIONS: Incidences of UTI and associated mortality are high in PLWD, especially in those with diabetes and dementia. Delayed treatment for UTI is further associated with high mortality.

From Gel to Crystal: Mechanism of HfO2 and ZrO2 Nanocrystal Synthesis in Benzyl Alcohol.

Nonaqueous sol-gel syntheses have been used to make many types of metal oxide nanocrystals. According to the current paradigm, nonaqueous syntheses have slow kinetics, thus favoring the thermodynamic (crystalline) product. Here we investigate the synthesis of hafnium (and zirconium) oxide nanocrystals from the metal chloride in benzyl alcohol. We follow the transition from precursor to nanocrystal through a combination of rheology, EXAFS, NMR, TEM, and X-ray total scattering (PDF analysis). Upon dissolving the metal chloride precursor, the exchange of chloride ligands for benzylalkoxide liberates HCl. The latter catalyzes the etherification of benzyl alcohol, eliminating water. During the temperature ramp to the reaction temperature (220 °C), sufficient water is produced to turn the reaction mixture into a macroscopic gel. Rheological analysis shows a network consisting of strong interactions with temperature-dependent restructuring. After a few minutes at the reaction temperature, crystalline particles emerge from the gel, and nucleation and growth are complete after 30 min. In contrast, 4 h are required to obtain the highest isolated yield, which we attribute to the slow in situ formation of water (the extraction solvent). We used our mechanistic insights to optimize the synthesis, achieving high isolated yields with a reduced reaction time. Our results oppose the idea that nonaqueous sol-gel syntheses necessarily form crystalline products in one step, without a transient, amorphous gel state.

Influence of Composition and Structure on the Optoelectronic Properties of Photocatalytic Bi4NbO8Cl-Bi2GdO4Cl Intergrowths.

Mixed metal oxyhalides are an exciting class of photocatalysts, capable of the sustainable generation of fuels and remediation of pollutants with solar energy. Bismuth oxyhalides of the types Bi4MO8X (M = Nb and Ta; X = Cl and Br) and Bi2AO4X (A = most lanthanides; X = Cl, Br, and I) have an electronic structure that imparts photostability, as their valence band maxima (VBM) are composed of O 2p orbitals rather than X np orbitals that typify many other bismuth oxyhalides. Here, flux-based synthesis of intergrowth Bi4NbO8Cl-Bi2GdO4Cl is reported, testing the hypothesis that both intergrowth stoichiometry and M identity serve as levers toward tunable optoelectronic properties. X-ray scattering and atomically resolved electron microscopy verify intergrowth formation. Facile manipulation of the Bi4NbO8Cl-to-Bi2GdO4Cl ratio is achieved with the specific ratio influencing both the crystal and electronic structures of the intergrowths. This compositional flexibility and crystal structure engineering can be leveraged for photocatalytic applications, with comparisons to the previously reported Bi4TaO8Cl-Bi2GdO4Cl intergrowth revealing how subtle structural and compositional features can impact photocatalytic materials.

The Chemistry of Spinel Ferrite Nanoparticle Nucleation, Crystallization, and Growth.

The nucleation, crystallization, and growth mechanisms of MnFe2O4, CoFe2O4, NiFe2O4, and ZnFe2O4 nanocrystallites prepared from coprecipitated transition metal (TM) hydroxide precursors treated at sub-, near-, and supercritical hydrothermal conditions have been studied by in situ X-ray total scattering (TS) with pair distribution function (PDF) analysis, and in situ synchrotron powder X-ray diffraction (PXRD) with Rietveld analysis. The in situ TS experiments were carried out on 0.6 M TM hydroxide precursors prepared from aqueous metal chloride solutions using 24.5% NH4OH as the precipitating base. The PDF analysis reveals equivalent nucleation processes for the four spinel ferrite compounds under the studied hydrothermal conditions, where the TMs form edge-sharing octahedrally coordinated hydroxide units (monomers/dimers and in some cases trimers) in the aqueous precursor, which upon hydrothermal treatment nucleate through linking by tetrahedrally coordinated TMs. The in situ PXRD experiments were carried out on 1.2 M TM hydroxide precursors prepared from aqueous metal nitrate solutions using 16 M NaOH as the precipitating base. The crystallization and growth of the nanocrystallites were found to progress via different processes depending on the specific TMs and synthesis temperatures. The PXRD data show that MnFe2O4 and CoFe2O4 nanocrystallites rapidly grow (typically <1 min) to equilibrium sizes of 20-25 nm and 10-12 nm, respectively, regardless of applied temperature in the 170-420 °C range, indicating limited possibility of targeted size control. However, varying the reaction time (0-30 min) and temperature (150-400 °C) allows different sizes to be obtained for NiFe2O4 (3-30 nm) and ZnFe2O4 (3-12 nm) nanocrystallites. The mechanisms controlling the crystallization and growth (nucleation, growth by diffusion, Ostwald ripening, etc.) were examined by qualitative analysis of the evolution in refined scale factor (proportional to extent of crystallization) and mean crystallite volume (proportional to extent of growth). Interestingly, lower kinetic barriers are observed for the formation of the mixed spinels (MnFe2O4 and CoFe2O4) compared to the inverse (NiFe2O4) and normal (ZnFe2O4) spinel structured compounds, suggesting that the energy barrier for formation may be lowered when the TMs have no site preference.

POMFinder: identifying polyoxometallate cluster structures from pair distribution function data using explainable machine learning.

Characterization of a material structure with pair distribution function (PDF) analysis typically involves refining a structure model against an experimental data set, but 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. Presented here is POMFinder, a machine learning (ML) classifier that rapidly screens a database of structures, here polyoxometallate (POM) clusters, to identify candidate structures for PDF data modelling. The approach is shown to identify suitable POMs from experimental data, including in situ data collected with fast acquisition times. This automated approach has significant potential for identifying suitable models for structure refinement to extract quantitative structural parameters in materials chemistry research. POMFinder is open source and user friendly, making it accessible to those without prior ML knowledge. It is also demonstrated that POMFinder offers a promising modelling framework for combined modelling of multiple scattering techniques.

ClusterFinder: a fast tool to find cluster structures from pair distribution function data.

A novel automated high-throughput screening approach, ClusterFinder, is reported for finding candidate structures for atomic pair distribution function (PDF) structural refinements. Finding starting models for PDF refinements is notoriously difficult when the PDF originates from nanoclusters or small nanoparticles. The reported ClusterFinder algorithm can screen 104 to 105 candidate structures from structural databases such as the Inorganic Crystal Structure Database (ICSD) in minutes, using the crystal structures as templates in which it looks for atomic clusters that result in a PDF similar to the target measured PDF. The algorithm returns a rank-ordered list of clusters for further assessment by the user. The algorithm has performed well for simulated and measured PDFs of metal-oxido clusters such as Keggin clusters. This is therefore a powerful approach to finding structural cluster candidates in a modelling campaign for PDFs of nanoparticles and nanoclusters.

Digital remote monitoring for screening and early detection of urinary tract infections.

Urinary Tract Infections (UTIs) are one of the most prevalent bacterial infections in older adults and a significant contributor to unplanned hospital admissions in People Living with Dementia (PLWD), with early detection being crucial due to the predicament of reporting symptoms and limited help-seeking behaviour. The most common diagnostic tool is urine sample analysis, which can be time-consuming and is only employed where UTI clinical suspicion exists. In this method development and proof-of-concept study, participants living with dementia were monitored via low-cost devices in the home that passively measure activity, sleep, and nocturnal physiology. Using 27828 person-days of remote monitoring data (from 117 participants), we engineered features representing symptoms used for diagnosing a UTI. We then evaluate explainable machine learning techniques in passively calculating UTI risk and perform stratification on scores to support clinical translation and allow control over the balance between alert rate and sensitivity and specificity. The proposed UTI algorithm achieves a sensitivity of 65.3% (95% Confidence Interval (CI) = 64.3-66.2) and specificity of 70.9% (68.6-73.1) when predicting UTIs on unseen participants and after risk stratification, a sensitivity of 74.7% (67.9-81.5) and specificity of 87.9% (85.0-90.9). In addition, feature importance methods reveal that the largest contributions to the predictions were bathroom visit statistics, night-time respiratory rate, and the number of previous UTI events, aligning with the literature. Our machine learning method alerts clinicians of UTI risk in subjects, enabling earlier detection and enhanced screening when considering treatment.

Formation of intermetallic PdIn nanoparticles: influence of surfactants on nanoparticle atomic structure.

Bimetallic nanoparticles have been extensively studied as electrocatalysts due to their superior catalytic activity and selectivity compared to their monometallic counterparts. The properties of bimetallic materials depend on the ordering of the metals in the structure, and to tailor-make materials for specific applications, it is important to be able to control the atomic structure of the materials during synthesis. Here, we study the formation of bimetallic palladium indium nanoparticles to understand how the synthesis parameters and additives used influence the atomic structure of the obtained product. Specifically, we investigate a colloidal synthesis, where oleylamine was used as the main solvent while the effect of two surfactants, oleic acid (OA) and trioctylphosphine (TOP) was studied. We found that without TOP included in the synthesis, a Pd-rich intermetallic phase with the Pd3In structure initially formed, which transformed into large NPs of the CsCl-structured PdIn phase. When TOP was included, the syntheses yielded both In2O3 and Pd3In. In situ X-ray total scattering with Pair Distribution Function analysis was used to study the formation process of PdIn bimetallic NPs. Our results highlight how seemingly subtle changes to material synthesis methods can have a large influence on the product atomic structure.

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 identify physical and structural models and extract information efficiently and accurately 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.

The more the better: on the formation of single-phase high entropy alloy nanoparticles as catalysts for the oxygen reduction reaction.

High entropy alloys (HEAs) are an important new material class with significant application potential in catalysis and electrocatalysis. The entropy-driven formation of HEA materials requires high temperatures and controlled cooling rates. However, catalysts in general also require highly dispersed materials, i.e., nanoparticles. Only then a favorable utilization of the expensive raw materials can be achieved. Several recently reported HEA nanoparticle synthesis strategies, therefore, avoid the high-temperature regime to prevent particle growth. In our work, we investigate a system of five noble metal single-source precursors with superior catalytic activity for the oxygen reduction reaction. Combining in situ X-ray powder diffraction with multi-edge X-ray absorption spectroscopy, we address the fundamental question of how single-phase HEA nanoparticles can form at low temperatures. It is demonstrated that the formation of HEA nanoparticles is governed by stochastic principles and the inhibition of precursor mobility during the formation process favors the formation of a single phase. The proposed formation principle is supported by simulations of the nanoparticle formation in a randomized process, rationalizing the experimentally found differences between two-element and multi-element metal precursor mixtures.

High-Entropy Oxides in the Mullite-Type Structure.

High-entropy materials (HEMs) represent a new class of solid solutions containing at least five different elements. Their compositional diversity makes them promising as platforms for the development of functional materials. We synthesized new HEMs in a mullite-type structure and present five compounds, i.e., Bi2(Al0.25Ga0.25Fe0.25Mn0.25)4O9 and A2Mn4O10 with variations of A = Nd, Sm, Y, Er, Eu, Ce, and Bi, demonstrating the vast accessible composition space. By combining scattering, microscopy, and spectroscopy techniques, we show that our materials are mixed solid solutions. Remarkably, when following their crystallization in situ using X-ray diffraction and X-ray absorption spectroscopy, we find that the HEMs form through a metastable amorphous phase without the formation of any crystalline intermediates. We expect that our synthesis is excellently suited to synthesizing diverse HEMs and therefore will have a significant impact on their future exploration.

Unveiling polyamorphism and polyamorphic interconversions in pharmaceuticals: the peculiar case of hydrochlorothiazide.

Polyamorphism has been a controversial and highly debated solid-state phenomenon in both material and pharmaceutical communities. Although some evidence of this fascinating phenomenon has been reported for several inorganic systems, and more recently also for a few organic compounds, the occurrence of polyamorphism is poorly understood and the molecular-level organization of polyamorphic forms is still unknown. Here we have investigated the occurrence of polyamorphism and polyamorphic interconversions in hydrochlorothiazide (HCT), using both experimental and computational methods. Three distinct HCT polyamorphs, presenting distinct physical and thermal stabilities as well as distinct relaxation properties, were systematically prepared using spray-drying (SD), quench-cooling (QC) and ball milling (BM) methods. HCT polyamorph II (obtained by QC) was found to be more physically stable than polyamorphs I and III (obtained by SD and BM, respectively). Furthermore, polyamorphs I and III could be converted into polyamorph II after QC, while polyamorph II did not convert to any other polyamorph after SD or BM. Molecular dynamics simulations show that HCT dihedral angle distributions are significantly different for polyamorphs I and II, which is postulated as a possible explanation for their different physicochemical properties.

First Insights into Population Structure and Genetic Diversity Versus Host Specificity in Trypanorhynch Tapeworms Using Multiplexed Shotgun Genotyping.

Theory predicts relaxed host specificity and high host vagility should contribute to reduced genetic structure in parasites while strict host specificity and low host vagility should increase genetic structure. Though these predictions are intuitive, they have never been explicitly tested in a population genomic framework. Trypanorhynch tapeworms, which parasitize sharks and rays (elasmobranchs) as definitive hosts, are the only order of elasmobranch tapeworms that exhibit considerable variability in their definitive host specificity. This allows for unique combinations of host use and geographic range, making trypanorhynchs ideal candidates for studying how these traits influence population-level structure and genetic diversity. Multiplexed shotgun genotyping (MSG) data sets were generated to characterize component population structure and infrapopulation diversity for a representative of each trypanorhynch suborder: the ray-hosted Rhinoptericola megacantha (Trypanobatoida) and the shark-hosted Callitetrarhynchus gracilis (Trypanoselachoida). Adults of R. megacantha are more host-specific and less broadly distributed than adults of C. gracilis, allowing correlation between these factors and genetic structure. Replicate tapeworm specimens were sequenced from the same host individual, from multiple conspecific hosts within and across geographic regions, and from multiple definitive host species. For R. megacantha, population structure coincided with geography rather than host species. For C. gracilis, limited population structure was found, suggesting a potential link between degree of host specificity and structure. Conspecific trypanorhynchs from the same host individual were found to be as, or more, genetically divergent from one another as from conspecifics from different host individuals. For both species, high levels of homozygosity and positive FIS values were documented.

Influence of the 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, WO3·0.5H2O, 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 suggests that the precursor structure just prior to crystallization can be used as a tool for targeting specific crystalline phases of interest.

Control of H-Related Defects in γ-MnO2 in a Hydrothermal Synthesis.

Manganese dioxide is a good candidate for effective energy storage and conversion as it possesses rich electrochemistry. The compound also shows a wide polymorphism. The γ-variety, an intergrowth of ß- and R-MnO2, has been extensively studied in several types of batteries (e.g., Zn/MnO2, Li-ion) and is a common electrode material for commercial batteries. It is well known that the insertion of protons thermodynamically stabilizes γ-MnO2 with respect to ß-MnO2. Protons can enter the structure either by forming groups of 4 hydroxyls around a Mn4+ vacancy, called a Ruetschi defect, or by forming a hydroxyl group near a Mn3+ ion, called a Coleman defect. These defects differently affect the electrochemistry of manganese oxide, and tailoring their amount in the structure can be used to tune the material properties. Previous studies have addressed the proton insertion process, but the role of the synthesis pathway on the amount of defects created is not well understood. We here investigate how the parameters in a hydrothermal synthesis of γ-MnO2 nanoparticles influence the amount and type of H-related defects. Structural investigations are carried out using Pair Distribution Function analysis, X-ray absorption spectroscopy, thermogravimetric analysis, and inelastic neutron scattering. We demonstrate the possibility to control the amount and type of defects introduced during the synthesis. While the amount of Ruetschi defects increases with synthesis temperature, it decreases with extended synthesis time, along with the amount of Coleman defects. Moreover, we discuss the arrangement of the defects in the γ-MnO2 nanoparticles.

A 3D-printed flow-cell for on-grid purification of electron microscopy samples directly from lysate.

While recent advances in cryo-EM, coupled with single particle analysis, have the potential to allow structure determination in a near-native state from vanishingly few individual particles, this vision has yet to be realised in practise. Requirements for particle numbers that currently far exceed the theoretical lower limits, challenges with the practicalities of achieving high concentrations for difficult-to-produce samples, and inadequate sample-dependent imaging conditions, all result in significant bottlenecks preventing routine structure determination using cryo-EM. Therefore, considerable efforts are being made to circumvent these bottlenecks by developing affinity purification of samples on-grid; at once obviating the need to produce large amounts of protein, as well as more directly controlling the variable, and sample-dependent, process of grid preparation. In this proof-of-concept study, we demonstrate a further practical step towards this paradigm, developing a 3D-printable flow-cell device to allow on-grid affinity purification from raw inputs such as whole cell lysates, using graphene oxide-based affinity grids. Our flow-cell device can be interfaced directly with routinely-used laboratory equipment such as liquid chromatographs, or peristaltic pumps, fitted with standard chromatographic (1/16") connectors, and can be used to allow binding of samples to affinity grids in a controlled environment prior to the extensive washing required to remove impurities. Furthermore, by designing a device which can be 3D printed and coupled to routinely used laboratory equipment, we hope to increase the accessibility of the techniques presented herein to researchers working towards single-particle macromolecular structures.

Influence of Temperature on the Performance of Carbon- and ATO-supported Oxygen Evolution Reaction Catalysts in a Gas Diffusion Electrode Setup.

State-of-the-art industrial electrocatalysts for the oxygen evolution reaction (OER) under acidic conditions are Ir-based. Considering the scarce supply of Ir, it is imperative to use the precious metal as efficiently as possible. In this work, we immobilized ultrasmall Ir and Ir0.4Ru0.6 nanoparticles on two different supports to maximize their dispersion. One high-surface-area carbon support serves as a reference but has limited technological relevance due to its lack of stability. The other support, antimony-doped tin oxide (ATO), has been proposed in the literature as a possible better support for OER catalysts. Temperature-dependent measurements performed in a recently developed gas diffusion electrode (GDE) setup reveal that surprisingly the catalysts immobilized on commercial ATO performed worse than their carbon-immobilized counterparts. The measurements suggest that the ATO support deteriorates particularly fast at elevated temperatures.

Sub-second pair distribution function using a broad bandwidth monochromator.

Here the use of a broad energy bandwidth monochromator, i.e. a pair of B4C/W multilayer mirrors (MLMs), is demonstrated for X-ray total scattering (TS) measurements and pair distribution function (PDF) analysis. Data are collected both on powder samples and from metal oxo clusters in aqueous solution at various concentrations. A comparison between the MLM PDFs and those obtained using a standard Si(111) double-crystal monochromator shows that the measurements yield MLM PDFs of high quality which are suitable for structure refinement. Moreover, the effects of time resolution and concentration on the quality of the resulting PDFs of the metal oxo clusters are investigated. PDFs of heptamolybdate clusters and tungsten α-Keggin clusters from X-ray TS data were obtained with a time resolution down to 3 ms and still showed a similar level of Fourier ripples to PDFs obtained from 1 s measurements. This type of measurement could thus open up faster time-resolved TS and PDF studies.

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

Material nucleation processes are poorly understood; nevertheless, an atomistic understanding of material formation would aid in the design of material synthesis methods. Here, we apply in situ X-ray total scattering experiments with pair distribution function (PDF) analysis to study the hydrothermal synthesis of wolframite-type MWO4 (M : Mn, Fe, Co, Ni). The data obtained allow the mapping of the material formation pathway in detail. We first show that upon mixing of the aqueous precursors, a crystalline precursor containing [W8O27]6- clusters forms for the MnWO4 synthesis, while amorphous pastes form for the FeWO4, CoWO4 and NiWO4 syntheses. The structure of the amorphous precursors was studied in detail with PDF analysis. Using database structure mining and an automated modelling strategy by applying machine learning, we show that the amorphous precursor structure can be described through polyoxometalate chemistry. A skewed sandwich cluster containing Keggin fragments describes the PDF of the precursor structure well, and the analysis shows that the precursor for FeWO4 is more ordered than that of CoWO4 and NiWO4. Upon heating, the crystalline MnWO4 precursor quickly converts directly to crystalline MnWO4, while the amorphous precursors transform into a disordered intermediate phase before the crystalline tungstates appear. Our data show that the more disordered the precursor is, the longer the reaction time required to form crystalline products, and disorder in the precursor phase appears to be a barrier for crystallization. More generally, we see that polyoxometalate chemistry is useful when describing the initial wet-chemical formation of mixed metal oxides.