There's no place like real-space: elucidating size-dependent atomic structure of nanomaterials using pair distribution function analysis

The ordered lattice host framework induced guest Li+ disordering in high performance cobalt-free Ni-rich cathode materials

Recently, due to high price, resource shortage and unstable supply of cobalt, the development of low-cost cobalt-free Ni-rich cathodes has attracted extensive attention with the ever-increasing lithium-ion batteries (LIBs) industry. Selecting cost-effective elements to replace cobalt in Ni-rich cathodes is urgent. However, the principle of structural design of Ni-rich cathode remains unclear, hampering the selection of alternative elements. Herein, the cobalt-free cathodes of LiNi0.95Mg0.05O2 (NiMg) and LiNi0.95Mn0.05O2 (NiMn) are designed as alternatives to LiNi0.96Co0.04O2 (NiCo). NiMg has comparable cycle stability with NiCo, while NiMn has inferior cycle performance. Reverse Monte Carlo modelling was used to generate structural model and uncover local structure by fitting pair distribution function. It reveals Mn causes more severe Jahn-Teller distortions and disordered lattice host framework (Ni0.95M0.05O2, M = Co/Mn/Mg) than Co and Mg due to the strong size effect and coulomb interactions of Mn in Ni0.95Mn0.05O2 layer. The outstanding cycle stability of NiMg and NiCo originates from the ordered lattice host frameworks, which relieve stress and inhibit particle breakage during cycle. Meanwhile, the ordered lattice host framework induced guest Li+ disordering reduces Li+ diffusion energy barrier, improving the rate capability. This study provides a new perspective for the structural design of cobalt-free Ni-rich cathodes.

Behavior of Microstrain in Nd3+-Sensitized Near-Infrared Upconverting Core-Shell Nanocrystals for Defect-Induced Tailoring of Luminescence Intensity

In an attempt to optimize the upconversion luminescence (UCL) output of a Nd3+-sensitized near-infrared (808 nm) upconverting core-shell (CS) nanocrystal through deliberate incorporation of lattice defects, a comprehensive analysis of microstrain both at the CS interface and within the core layer was performed using integral breadth calculation of high-energy synchrotron X-ray (λ = 0.568551 Å) diffraction. An atomic level interpretation of such microstrain was performed using pair distribution function analysis of the high-energy total scattering. The core NC developed compressive microstrain, which gradually transformed into tensile microstrain with the growth of the epitaxial shell. Such a reversal was rationalized in terms of a consistent negative lattice mismatch. Upon introduction of lattice defects into the CS systems upon incorporation of Li+, the corresponding UCL intensity was maximized at some specific Li+ incorporation, where the tensile microstrain of CS, compressive microstrain of the core, and atomic level disorders exhibited their respective extreme values irrespective of the activator ions.

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.

Nanoscale Stabilization Mechanism of Sodium Sulfate Decahydrate at Polyelectrolyte Interfaces

Sodium sulfate decahydrate (SSD) is a low-cost phase-change material (PCM) for thermal energy storage applications that offers substantial melting enthalpy and a suitable temperature range for near-ambient applications. However, SSD's consistent phase separation with decreased melting enthalpy over repeated thermal cycles limits its application as a PCM. Sulfonated polyelectrolytes, such as dextran sulfate sodium (DSS), have shown great effectiveness in preventing phase separation in SSD. However, there is limited understanding of the stabilization mechanism of SSD by DSS at the atomic length and time scales. In this work, we investigate SSD stabilization via DSS using neutron scattering and molecular dynamics (MD) simulations. Neutron scattering and pair distribution function analysis revealed the structural evolution of the PCM samples below and above the phase change temperatures. MD simulations revealed that water from the hydrate structure migrates from the hydrate crystal to the SSD-DSS interfacial region upon melting. The water is stabilized at this interface by aggregation around the hydrophilic sulfonic acid groups attached to the backbone of the polyelectrolyte. This architecture retains water near the dehydrated sodium sulfate, preventing phase separation and, consequently, stabilizing SSD rehydration. This work provides atomistic insight into selecting and designing stable and high-performance PCMs for heating and cooling applications in building technologies.

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.

Nanotechnology, a frontier in agricultural science, a novel approach in abiotic stress management and convergence with new age medicine-A review

Climate change imposes various environmental stresses which substantially impact plant growth and productivity. Salinity, drought, temperature extremes, heavy metals, and nutritional imbalances are among several abiotic stresses contributing to high yield losses of crops in various parts of the world, resulting in food insecurity. Many interesting strategies are being researched in the attempt to improve plants' environmental stress tolerance. These include the application of nanoparticles, which have been found to improve plant function under stress situations. Nanotechnology will be a key driver in the upcoming agri-tech and pharmaceutical revolution, which promises a more sustainable, efficient, and resilient agricultural and medical system Nano-fertilizers can help plants utilise nutrients more efficiently by releasing nutrients slowly and sustainably. Plant physiology and nanomaterial features (such as size, shape, and charge) are important aspects influencing the impact on plant growth. Here, we discussed the most promising new opportunities and methodologies for using nanotechnology to increase the efficiency of critical inputs for crop agriculture, as well as to better manage biotic and abiotic stress. Potential development and implementation challenges are highlighted, emphasising the importance of designing suggested nanotechnologies using a systems approach. Finally, the strengths, flaws, possibilities, and risks of nanotechnology are assessed and analysed in order to present a comprehensive and clear picture of the nanotechnology potentials, as well as future paths for nano-based agri-food applications towards sustainability. Future research directions have been established in order to support research towards the long-term development of nano-enabled agriculture and evolution of pharmaceutical industry.

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.

Structure and Role of a Ga-Promoter in Ni-Based Catalysts for the Selective Hydrogenation of CO2 to Methanol

Supported, bimetallic catalysts have shown great promise for the selective hydrogenation of CO2 to methanol. In this study, we decipher the catalytically active structure of Ni-Ga-based catalysts. To this end, model Ni-Ga-based catalysts, with varying Ni:Ga ratios, were prepared by a surface organometallic chemistry approach. In situ differential pair distribution function (d-PDF) analysis revealed that catalyst activation in H2 leads to the formation of nanoparticles based on a Ni-Ga face-centered cubic (fcc) alloy along with a small quantity of GaOx. Structure refinements of the d-PDF data enabled us to determine the amount of both alloyed Ga and GaOx species. In situ X-ray absorption spectroscopy experiments confirmed the presence of alloyed Ga and GaOx and indicated that alloying with Ga affects the electronic structure of metallic Ni (viz., Niδ-). Both the Ni:Ga ratio in the alloy and the quantity of GaOx are found to minimize methanation and to determine the methanol formation rate and the resulting methanol selectivity. The highest formation rate and methanol selectivity are found for a Ni-Ga alloy having a Ni:Ga ratio of ∼75:25 along with a small quantity of oxidized Ga species (0.14 molNi-1). Furthermore, operando infrared spectroscopy experiments indicate that GaOx species play a role in the stabilization of formate surface intermediates, which are subsequently further hydrogenated to methoxy species and ultimately to methanol. Notably, operando XAS shows that alloying between Ni and Ga is maintained under reaction conditions and is key to attaining a high methanol selectivity (by minimizing CO and CH4 formation), while oxidized Ga species enhance the methanol formation rate.

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.

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.

Charting Nanocluster Structures via Convolutional Neural Networks

A general method to obtain a representation of the structural landscape of nanoparticles in terms of a limited number of variables is proposed. The method is applied to a large data set of parallel tempering molecular dynamics simulations of gold clusters of 90 and 147 atoms, silver clusters of 147 atoms, and copper clusters of 147 atoms, covering a plethora of structures and temperatures. The method leverages convolutional neural networks to learn the radial distribution functions of the nanoclusters and distills a low-dimensional chart of the structural landscape. This strategy is found to give rise to a physically meaningful and differentiable mapping of the atom positions to a low-dimensional manifold in which the main structural motifs are clearly discriminated and meaningfully ordered. Furthermore, unsupervised clustering on the low-dimensional data proved effective at further splitting the motifs into structural subfamilies characterized by very fine and physically relevant differences such as the presence of specific punctual or planar defects or of atoms with particular coordination features. Owing to these peculiarities, the chart also enabled tracking of the complex structural evolution in a reactive trajectory. In addition to visualization and analysis of complex structural landscapes, the presented approach offers a general, low-dimensional set of differentiable variables that has the potential to be used for exploration and enhanced sampling purposes.

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.

In situ synchrotron X-ray total scattering measurements and analysis of colloidal CsPbX3 nanocrystals during flow synthesis

In situ X-ray scattering measurements of CsPbX3 (X = Cl, Br, I) nanocrystal formation and halide exchange at NSLS-II beamlines were performed in an automated flow reactor. Total scattering measurements were performed at the 28-ID-2 (XPD) beamline and small-angle X-ray scattering at the 16-ID (LiX) beamline. Nanocrystal structural parameters of interest, including size, size distribution and atomic structure, were extracted from modeling the total scattering data. The results highlight the potential of these beamlines and the measurement protocols described in this study for studying dynamic processes of colloidal nanocrystal synthesis in solution with timescales on the order of seconds.

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.

Structural Analysis and Intrinsic Enzyme Mimicking Activities of Ligand-Free PtAg Nanoalloys

Nanozymes are nanomaterials with biocatalytic properties under physiological conditions and are one class of artificial enzymes to overcome the high cost and low stability of natural enzymes. However, surface ligands on nanomaterials will decrease the catalytic activity of the nanozymes by blocking the active sites. To address this limitation, ligand-free PtAg nanoclusters (NCs) are synthesized and applied as nanozymes for various enzyme-mimicking reactions. By taking advantage of the mutual interaction of zeolitic imidazolate frameworks (ZIF-8) and Pt precursors, a good dispersion of PtAg bimetal NCs with a diameter of 1.78 ± 0.1 nm is achieved with ZIF-8 as a template. The incorporation of PtAgNCs in the voids of ZIF-8 is confirmed with structural analysis using the atomic pair-distribution function and powder X-ray diffraction. Importantly, the PtAgNCs present good catalytic activity for various enzyme-mimicking reactions, including peroxidase-/catalase- and oxidase-like reactions. Further, this work compares the catalytic activity between PtAg NCs and PtAg nanoparticles with different compositions and finds that these two nanozymes present a converse dependency of Ag-loading on their activity. This study contributes to the field of nanozymes and presents a potential option to prepare ligand-free bimetal biocatalysts with sizes in the nanocluster regime.

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

Structure solution of nanostructured materials that have limited long-range order remains a bottleneck in materials development. We present a deep learning algorithm, DeepStruc, that can solve a simple monometallic nanoparticle structure directly from a Pair Distribution Function (PDF) obtained from total scattering data by using a conditional variational autoencoder. 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.

Chemical Insights into the Formation of Colloidal Iridium Nanoparticles from In Situ X-ray Total Scattering: Influence of Precursors and Cations on the Reaction Pathway

Iridium nanoparticles are important catalysts for several chemical and energy conversion reactions. Studies of iridium nanoparticles have also been a key for the development of kinetic models of nanomaterial formation. However, compared to other metals such as gold or platinum, knowledge on the nature of prenucleation species and structural insights into the resultant nanoparticles are missing, especially for nanoparticles obtained from Ir_xCl_y precursors investigated here. We use in situ X-ray total scattering (TS) experiments with pair distribution function (PDF) analysis to study a simple, surfactant-free synthesis of colloidal iridium nanoparticles. The reaction is performed in methanol at 50 °C with only a base and an iridium salt as precursor. From different precursor salts─IrCl₃, IrCl₄, H₂IrCl₆, or Na₂IrCl₆─colloidal nanoparticles as small as Ir_∼55 are obtained as the final product. The nanoparticles do not show the bulk iridium face-centered cubic (fcc) structure but show decahedral and icosahedral structures. The formation route is highly dependent on the precursor salt used. Using IrCl₃ or IrCl₄, metallic iridium nanoparticles form rapidly from Ir_xCl_y^n- complexes, whereas using H₂IrCl₆ or Na₂IrCl₆, the iridium nanoparticle formation follows a sudden growth after an induction period and the brief appearance of a crystalline phase. With H₂IrCl₆, the formation of different Ir_n (n = 55, 55, 85, and 116) nanoparticles depends on the nature of the cation in the base (LiOH, NaOH, KOH, or CsOH, respectively) and larger particles are obtained with larger cations. As the particles grow, the nanoparticle structure changes from partly icosahedral to decahedral. The results show that the synthesis of iridium nanoparticles from Ir_xCl_y is a valuable iridium nanoparticle model system, which can provide new compositional and structural insights into iridium nanoparticle formation and growth.

Fatty acid capped, metal oxo clusters as the smallest conceivable nanocrystal prototypes

Metal oxo clusters of the type M₆O₄(OH)₄(OOCR)₁₂ (M = Zr or Hf) are valuable building blocks for materials science. Here, we synthesize a series of zirconium and hafnium oxo clusters with ligands that are typically used to stabilize oxide nanocrystals (fatty acids with long and/or branched chains). The fatty acid capped oxo clusters have a high solubility but do not crystallize, precluding traditional purification and single-crystal XRD analysis. We thus develop alternative purification strategies and we use X-ray total scattering and Pair Distribution Function (PDF) analysis as our main method to elucidate the structure of the cluster core. We identify the correct structure from a series of possible clusters (Zr3, Zr4, Zr6, Zr12, Zr10, and Zr26). Excellent refinements are only obtained when the ligands are part of the structure model. Further evidence for the cluster composition is provided by nuclear magnetic resonance (NMR), infrared spectroscopy (FTIR), thermogravimetry analysis (TGA), and mass spectrometry (MS). We find that hydrogen bonded carboxylic acid is an intrinsic part of the oxo cluster. Using our analytical tools, we elucidate the conversion from a Zr6 monomer to a Zr12 dimer (and vice versa), induced by carboxylate ligand exchange. Finally, we compare the catalytic performance of Zr12-oleate clusters with oleate capped, 5.5 nm zirconium oxide nanocrystals in the esterification of oleic acid with ethanol. The oxo clusters present a five times higher reaction rate, due to their higher surface area. Since the oxo clusters are the lower limit of downscaling oxide nanocrystals, we present them as appealing catalytic materials, and as atomically precise model systems. In addition, the lessons learned regarding PDF analysis are applicable to other areas of cluster science as well, from semiconductor and metal clusters, to polyoxometalates.

In Situ Kinetic Observations on Crystal Nucleation and Growth

Nucleation and growth are critical steps in crystallization, which plays an important role in determining crystal structure, size, morphology, and purity. Therefore, understanding the mechanisms of nucleation and growth is crucial to realize the controllable fabrication of crystalline products with desired and reproducible properties. Based on classical models, the initial crystal nucleus is formed by the spontaneous aggregation of ions, atoms, or molecules, and crystal growth is dependent on the monomer's diffusion and the surface reaction. Recently, numerous in situ investigations on crystallization dynamics have uncovered the existence of nonclassical mechanisms. This review provides a summary and highlights the in situ studies of crystal nucleation and growth, with a particular emphasis on the state-of-the-art research progress since the year 2016, and includes technological advances, atomic-scale observations, substrate- and temperature-dependent nucleation and growth, and the progress achieved in the various materials: metals, alloys, metallic compounds, colloids, and proteins. Finally, the forthcoming opportunities and challenges in this fascinating field are discussed.

Effects of Grain Refinement and Thermal Aging on Atomic Scale Local Structures of Ultra-Fine Explosives by X-ray Total Scattering

The atomic scale local structures affect the initiation performance of ultra-fine explosives according to the stimulation results of hot spot formation. However, the experimental characterization of local structures in ultra-fine explosives has been rarely reported, due to the difficulty in application of characterization methods having both high resolution in and small damage to unstable organic explosive materials. In this work, X-ray total scattering was explored to investigate the atomic scale local distortion of two widely applicable ultra-fine explosives, LLM-105 and HNS. The experimental spectra of atomic pair distribution function (PDF) derived from scattering results were fitted by assuming rigid ring structures in molecules. The effects of grain refinement and thermal aging on the atomic scale local structure were investigated, and the changes in both the length of covalent bonds have been identified. Results indicate that by decreasing the particle size of LLM-105 and HNS from hundreds of microns to hundreds of nanometers, the crystal structures remain, whereas the molecular configuration slightly changes and the degree of structural disorder increases. For example, the average length of covalent bonds in LLM-105 reduces from 1.25 Å to 1.15 Å, whereas that in HNS increases from 1.25 Å to 1.30 Å, which is possibly related to the incomplete crystallization process and internal stress. After thermal aging of ultra-fine LLM-105 and HNS, the degree of structural disorder decreases, and the distortion in molecules formed in the synthesis process gradually healed. The average length of covalent bonds in LLM-105 increases from 1.15 Å to 1.27 Å, whereas that in HNS reduces from 1.30 Å to 1.20 Å. The possible reason is that the atomic vibration in the molecule intensifies during the heat aging treatment, and the internal stress was released through changes in molecular configuration, and thus the atomic scale distortion gradually heals. The characterization method and findings in local structures obtained in this work may pave the path to deeply understand the relationship between the defects and performance of ultra-fine explosives.

Hierarchically porous and mechanically stable monoliths from ordered mesoporous silica and their water filtration potential

Mechanically stable structures with interconnected hierarchical porosity combine the benefits of both small and large pores, such as high surface area, pore volume, and good mass transport capabilities. Hence, lightweight micro-/meso-/macroporous monoliths are prepared from ordered mesoporous silica COK-12 by means of spark plasma sintering (SPS, S-sintering) and compared to conventionally (C-) sintered monoliths. A multi-scale model is developed to fit the small angle X-ray scattering data and obtain information on the hexagonal lattice parameters, pore sizes from the macro to the micro range, as well as the dimensions of the silica population. For both sintering techniques, the overall mesoporosity, hexagonal pore ordering, and amorphous character are preserved. The monoliths' porosity (77-49%), mesopore size (6.2-5.2 nm), pore volume (0.50-0.22 g cm^-3), and specific surface area (451-180 m² g^-1) decrease with increasing processing temperature and pressure. While the difference in porosity is enhanced, the structural parameters between the C-and S-sintered monoliths are largely converging at 900 °C, except for the mesopore size and lattice parameter, whose dimensions are more extensively preserved in the S-sintered monoliths, however, coming along with larger deviations from the theoretical lattice. Their higher mechanical properties (biaxial strength up to 49 MPa, 724 MPa HV 9.807 N) at comparable porosities and ability to withstand ultrasonic treatment and dead-end filtration up to 7 bar allow S-sintered monoliths to reach a high permeance (2634 L m^-2 h^-1 bar^-1), permeability (1.25 × 10^-14 m²), and ability to reduce the chemical oxygen demand by 90% during filtration of a surfactant-stabilized oil in water emulsion, while indicating reasonable resistance towards fouling.

Understanding the Re-entrant Phase Transition in a Non-magnetic Scheelite

The stereochemical activity of lone pair electrons plays a central role in determining the structural and electronic properties of both chemically simple materials such as H₂O, as well as more complex condensed phases such as photocatalysts or thermoelectrics. TlReO₄ is a rare example of a non-magnetic material exhibiting a re-entrant phase transition and emphanitic behavior in the long-range structure. Here, we describe the role of the Tl⁺ 6s² lone pair electrons in these unusual phase transitions and illustrate its tunability by chemical doping, which has broad implications for functional materials containing lone pair bearing cations. First-principles density functional calculations clearly show the contribution of the Tl⁺ 6s² in the valence band region. Local structure analysis, via neutron total scattering, revealed that changes in the long-range structure of TlReO₄ occur due to changes in the correlation length of the Tl⁺ lone pairs. This has a significant effect on the anion interactions, with long-range ordered lone pairs creating a more densely packed structure. This resulted in a trade-off between anionic repulsions and lone pair correlations that lead to symmetry lowering upon heating in the long-range structure, whereby lattice expansion was necessary for the Tl⁺ lone pairs to become highly correlated. Similarly, introducing lattice expansion through chemical pressure allowed long-range lone pair correlations to occur over a wider temperature range, demonstrating a method for tuning the energy landscape of lone pair containing functional materials.

Osmium and OsO x nanoparticles: an overview of syntheses and applications

Precious metal nanoparticles are key for a range of applications ranging from catalysis and sensing to medicine. While gold (Au), silver (Ag), platinum (Pt), palladium (Pd) or ruthenium (Ru) nanoparticles have been widely studied, other precious metals are less investigated. Osmium (Os) is one of the least studied of the precious metals. However, Os nanoparticles are interesting materials since they present unique features compared to other precious metals and Os nanomaterials have been reported to be useful for a range of applications, catalysis or sensing for instance. With the increasing availability of advanced characterization techniques, investigating the properties of relatively small Os nanoparticles and clusters has become easier and it can be expected that our knowledge on Os nanomaterials will increase in the coming years. This review aims to give an overview on Os and Os oxide materials syntheses and applications.

Nanocomposite Concept for Electrochemical In Situ Preparation of Pt-Au Alloy Nanoparticles for Formic Acid Oxidation

Herein, we report a straightforward approach for the in situ preparation of Pt-Au alloy nanoparticles from Pt + xAu/C nanocomposites using monometallic colloidal nanoparticles as starting blocks. Four different compositions with fixed Pt content and varying Pt to Au mass ratios from 1:1 up to 1:7 were prepared as formic acid oxidation reaction (FAOR) catalysts. The study was carried out in a gas diffusion electrode (GDE) setup. It is shown that the presence of Au in the nanocomposites substantially improves the FAOR activity with respect to pure Pt/C, which serves as a reference. The nanocomposite with a mass ratio of 1:5 between Pt and Au displays the best performance during potentiodynamic tests, with the electro-oxidation rates, overpotential, and poisoning resistance being improved simultaneously. By comparison, too low or too high Au contributions in the nanocomposites lead to an unbalanced performance in the FAOR. The combination of operando small-angle X-ray scattering (SAXS), scanning transmission electron microscopy (STEM) elemental mapping, and wide-angle X-ray scattering (WAXS) reveals that for the nanocomposite with a 1:5 mass ratio, a conversion between Pt and Au from separate nanoparticles to alloy nanoparticles occurs during continuous potential cycling in formic acid. By comparison, the nanocomposites with lower Au contents, for example, 1:2, exhibit less in situ alloying, and the concomitant performance improvement is less pronounced. On applying identical location transmission electron microscopy (IL-TEM), it is revealed that the in situ alloying is due to Pt dissolution and re-deposition onto Au as well as Pt migration and coalescence with Au nanoparticles.

Monoalkyl Phosphinic Acids as Ligands in Nanocrystal Synthesis

Ligands play a crucial role in the synthesis of colloidal nanocrystals. Nevertheless, only a handful molecules are currently used, oleic acid being the most typical example. Here, we show that monoalkyl phosphinic acids are another interesting ligand class, forming metal complexes with a reactivity that is intermediate between the traditional carboxylates and phosphonates. We first present the synthesis of n-hexyl, 2-ethylhexyl, n-tetradecyl, n-octadecyl, and oleylphosphinic acid. These compounds are suitable ligands for high-temperature nanocrystal synthesis (240-300 °C) since, in contrast to phosphonic acids, they do not form anhydride oligomers. Consequently, CdSe quantum dots synthesized with octadecylphosphinic acid are conveniently purified, and their UV-vis spectrum is free from background scattering. The CdSe nanocrystals have a low polydispersity and a photoluminescence quantum yield up to 18% (without shell). Furthermore, we could synthesize CdSe and CdS nanorods using phosphinic acid ligands with high shape purity. We conclude that the reactivity toward TOP-S and TOP-Se precursors decreases in the following series: cadmium carboxylate > cadmium phosphinate > cadmium phosphonate. By introducing a third and intermediate class of surfactants, we enhance the versatility of surfactant-assisted syntheses.

Atomic structure of an FeCrMoCBY metallic glass revealed by high energy x-ray diffraction

Amorphous bulk metallic glasses with the composition Fe₄₈Cr₁₅Mo₁₄C₁₅B₆Y₂have been of interest due to their special mechanical and electronic properties, including corrosion resistance, high yield-strength, large elasticity, catalytic performance, and soft ferromagnetism. Here, we apply a reverse Monte Carlo technique to unravel the atomic structure of these glasses. The pair-distribution functions for various atomic pairs are computed based on the high-energy x-ray diffraction data we have taken from an amorphous sample. Monte Carlo cycles are used to move the atomic positions until the model reproduces the experimental pair-distribution function. The resulting fitted model is consistent with ourab initiosimulations of the metallic glass. Our study contributes to the understanding of functional properties of Fe-based bulk metallic glasses driven by disorder effects.

Rhodium-Based Metal-Organic Polyhedra Assemblies for Selective CO2 Photoreduction

Heterogenization of molecular catalysts via their immobilization within extended structures often results in a lowering of their catalytic properties due to a change in their coordination sphere. Metal-organic polyhedra (MOP) are an emerging class of well-defined hybrid compounds with a high number of accessible metal sites organized around an inner cavity, making them appealing candidates for catalytic applications. Here, we demonstrate a design strategy that enhances the catalytic properties of dirhodium paddlewheels heterogenized within MOP (Rh-MOP) and their three-dimensional assembled supramolecular structures, which proved to be very efficient catalysts for the selective photochemical reduction of carbon dioxide to formic acid. Surprisingly, the catalytic activity per Rh atom is higher in the supramolecular structures than in its molecular sub-unit Rh-MOP or in the Rh-metal-organic framework (Rh-MOF) and yields turnover frequencies of up to 60 h^-1 and production rates of approx. 76 mmole formic acid per gram of the catalyst per hour, unprecedented in heterogeneous photocatalysis. The enhanced catalytic activity is investigated by X-ray photoelectron spectroscopy and electrochemical characterization, showing that self-assembly into supramolecular polymers increases the electron density on the active site, making the overall reaction thermodynamically more favorable. The catalyst can be recycled without loss of activity and with no change of its molecular structure as shown by pair distribution function analysis. These results demonstrate the high potential of MOP as catalysts for the photoreduction of CO₂ and open a new perspective for the electronic design of discrete molecular architectures with accessible metal sites for the production of solar fuels.

Surfactant-free syntheses and pair distribution function analysis of osmium nanoparticles

A surfactant-free synthesis of precious metal nanoparticles (NPs) performed in alkaline low-boiling-point solvents has been recently reported. Monoalcohols are here investigated as solvents and reducing agents to obtain colloidal Os nanoparticles by using low-temperature (<100 °C) surfactant-free syntheses. The effect of the precursor (OsCl₃ or H₂OsCl₆), precursor concentration (up to 100 mM), solvent (methanol or ethanol), presence or absence of a base (NaOH), and addition of water (0 to 100 vol %) on the resulting nanomaterials is discussed. It is found that no base is required to obtain Os nanoparticles as opposed to the case of Pt or Ir NPs. The robustness of the synthesis for a precursor concentration up to 100 mM allows for the performance of X-ray total scattering with pair distribution function (PDF) analysis, which shows that 1-2 nm hexagonal close packed (hcp) NPs are formed from chain-like [OsO _x Cl _y ] complexes.

Structural Analysis of Molecular Materials Using the Pair Distribution Function

This is a review of atomic pair distribution function (PDF) analysis as applied to the study of molecular materials. The PDF method is a powerful approach to study short- and intermediate-range order in materials on the nanoscale. It may be obtained from total scattering measurements using X-rays, neutrons, or electrons, and it provides structural details when defects, disorder, or structural ambiguities obscure their elucidation directly in reciprocal space. While its uses in the study of inorganic crystals, glasses, and nanomaterials have been recently highlighted, significant progress has also been made in its application to molecular materials such as carbons, pharmaceuticals, polymers, liquids, coordination compounds, composites, and more. Here, an overview of applications toward a wide variety of molecular compounds (organic and inorganic) and systems with molecular components is presented. We then present pedagogical descriptions and tips for further implementation. Successful utilization of the method requires an interdisciplinary consolidation of material preparation, high quality scattering experimentation, data processing, model formulation, and attentive scrutiny of the results. It is hoped that this article will provide a useful reference to practitioners for PDF applications in a wide realm of molecular sciences, and help new practitioners to get started with this technique.

Effects of Voigt diffraction peak profiles on the pair distribution function

Powder diffraction and pair distribution function (PDF) analysis are well established techniques for investigation of atomic configurations in crystalline materials, and the two are related by a Fourier transformation. In diffraction experiments, structural information, such as crystallite size and microstrain, is contained within the peak profile function of the diffraction peaks. However, the effects of the PXRD (powder X-ray diffraction) peak profile function on the PDF are not fully understood. Here, all the effects from a Voigt diffraction peak profile are solved analytically, and verified experimentally through a high-quality X-ray total scattering measurement on Ni powder. The Lorentzian contribution to the microstrain broadening is found to result in Voigt-shaped PDF peaks. Furthermore, it is demonstrated that an improper description of the Voigt shape during model refinement leads to overestimation of the atomic displacement parameter.

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 change at extreme length scales.

Atomic arrangements in an amorphous CoFeB ribbon extracted via an analysis of radial distribution functions

We discuss the atomic structure of amorphous ferromagnetic FeCoB alloys, which are used widely in spintronics applications. Specifically, we obtain the pair-distribution functions for various atomic pairs based on high-energy x-ray diffraction data taken from an amorphous Co₂₀Fe₆₁B₁₉specimen. We start our reverse Monte Carlo cycles to determine the disordered structure with a two-phase model in which a small amount of cobalt is mixed with Fe₂₃B₆as a second phase. The structure of the alloy is found to be heterogeneous, where the boron atoms drive disorder through the random occupation of the atomic network. Our analysis also indicates the presence of small cobalt clusters that are embedded in the iron matrix and percolating the latter throughout the structure. This morphology can explain the enhanced spin polarization observed in amorphous magnetic materials.

Formation and growth mechanism for niobium oxide nanoparticles: atomistic insight from in situ X-ray total scattering

Understanding the mechanisms for nanoparticle nucleation and growth is crucial for the development of tailormade nanomaterials. Here, we use X-ray total scattering and Pair Distribution Function analysis to follow the formation and growth of niobium oxide nanoparticles. We study the solvothermal synthesis from niobium chloride in benzyl alcohol, and through investigations of the influence of reaction temperature, a formation pathway can be suggested. Upon dissolution of niobium chloride in benzyl alcohol, octahedral [NbCl6-xOx] complexes form through exchange of chloride ligands. Heating of the solution results in polymerization, where larger clusters built from multiple edge-sharing [NbCl6-xOx] octahedra assemble. This leads to the formation of a nucleation cluster with the ReO3 type structure, which grows to form nanoparticles of the Wadsley-Roth type H-Nb2O5 structure, which in the bulk phase usually only forms at high temperature. Upon further growth, structural defects appear, and the presence of shear-planes in the structure appears highly dependent on nanoparticle size.

Impact of Solution Chemistry on Growth and Structural Features of Mo-Substituted Spinel Iron Oxides

The effect of crystallizing solution chemistry on the chemistry of subsequently as-grown materials was investigated for Mo-substituted iron oxides prepared by thermally activated co-precipitation. In the presence of Mo ions, we find that varying the oxidation state of the iron precursor from Fe(II) to Fe(III) causes a progressive loss of atomic long-range order with the stabilization of 2-4 nm particles for the sample prepared with Fe(III). The oxidation state of the Fe precursor also affects the distribution of Fe and Mo cations within the spinel structure. Increasing the Fe precursor oxidation state gives decreased Fe-ion occupation and increased Mo-ion occupation of tetrahedral sites, as revealed by the extended X-ray absorption fine structure. The stabilization of Mo within tetrahedral sites appears to be unexpected, considering the octahedral preferred coordination number of Mo(VI). The analysis of the atomic structure of the sample prepared with Fe(III) indicates a local ordering of vacancies and that the occupation of tetrahedral sites by Mo induces a contraction of the interatomic distances within the polyhedra as compared to Fe atoms. Moreover, the occupancy of Mo into the thermodynamic site preference of a Mo dopant in Fe₂O₃ assessed by density functional theory calculations points to a stronger preference for Mo substitution at octahedral sites. Hence, we suggest that the synthetized compound is thermodynamically metastable, that is, kinetically trapped. Such a state is suggested to be a consequence of the tetrahedral site occupation by Mo ions. The population of these sites, known to be reactive sites enabling particle growth, is concomitant with the stabilization of very small particles. We confirmed our hypothesis by using a blank experiment without Mo ions, further supporting the impact of tetrahedral Mo ions on the growth of iron oxide nanoparticles. Our findings provide new insights into the relationships between the Fe-chemistry of the crystallizing solution and the structural features of the as-grown Mo-substituted Fe-oxide materials.

From platinum atoms in molecules to colloidal nanoparticles: A review on reduction, nucleation and growth mechanisms

Platinum (Pt) is one of the most studied materials in catalysis today and considered for a wide range of applications: chemical synthesis, energy conversion, air treatment, water purification, sensing, medicine etc. As a limited and non-renewable resource, optimized used of Pt is key. Nanomaterial design offers multiple opportunities to make the most of Pt resources down to the atomic scale. In particular, colloidal syntheses of Pt nanoparticles are well documented and simple to implement, which accounts for the large interest in research and development. For further breakthroughs in the design of Pt nanomaterials, a deeper understanding of the intricate synthesis-structures-properties relations of Pt nanoparticles must be obtained. Understanding how Pt nanoparticles form from molecular precursors is both a challenging and rewarding area of investigation. It is directly relevant to develop improved Pt nanomaterials but is also a source of inspiration to design other precious metal nanostructures. Here, we review the current understanding of Pt nanoparticle formation. This review is aimed at readers with interest in Pt nanoparticles in general and their colloidal syntheses in particular. Readers with a strongest interest on the study of nanomaterial formation will find here the case study of Pt. The preferred model systems and characterization techniques used to perform the study of Pt nanoparticle syntheses are discussed. In light of recent achievements, further direction and areas of research are proposed.

Structural Disorder of Mechanically Activated δ-MgCl2 Studied by Synchrotron X-ray Total Scattering and Vibrational Spectroscopy

A combination of synchrotron X-ray total scattering and molecular simulation is a powerful approach for reliable determination of the structure of δ-MgCl2 as an indispensable component of heterogeneous Ziegler–Natta catalysts. Here, the same approach is applied to mechanically activated MgCl2. Four types of mechanically activated MgCl2 samples are prepared using ball-milling in the absence and presence of different donors. The development of structural disorder along the grinding time is compared. It was found that the presence of donors accelerates the formation of δ-MgCl2 in an early stage of grinding, while elongated grinding eventually results in δ-MgCl2 with similar extents of structural disorder in the absence and presence of different donors. The FT-IR investigation consistently verified the morphological similarity between the firmly ground samples. Thus, the structure of δ-MgCl2 is likely governed by mechanical energy when sufficiently ground.

Spectroscopy and scattering for chemistry: new possibilities and challenges with large scale facilities

Kirsten M. Ø. Jensen, Dorota Koziej and Serena DeBeer introduce the Nanoscale themed issue on spectroscopy and scattering for chemistry: new possibilities and challenges with large scale facilities.

Atomically Precise Nanocrystals

Nanocrystals are a state-of-matter in the border area between molecules and bulk materials. Unlike bulk materials, nanocrystals have size-dependent properties, yet the question remains whether nanocrystal properties can be analyzed, understood, and controlled with atomic precision, a key characteristic of molecules. Acknowledging the inclination of nanocrystals to form defect structures, we first outline the prospects of atomically precise analysis. A broad spectrum of analytical methods has become available over the last five years, such that for heterogeneous nanocrystal ensembles, a single, atomically precise representative structure can be determined to explore structure-property relations. Atomically precise synthesis, on the other hand, remains an outstanding challenge that may well face fundamental limitations. However, to amplify properties and prepare nanocrystals for specific applications, full atomic precision may not be needed. Examples of an atomic precision light approach, focusing on exact thickness or facet control, exist and can inspire scientists to explore atomic precision in nanocrystal research further.