Multicomponent Clusters Research Articles

Modular Cloning Tools for Streptomyces spp. and Application to the De Novo Biosynthesis of Flavokermesic Acid.

The filamentous Streptomyces are among the most prolific producers of bioactive natural products and are thus attractive chassis for the heterologous expression of native and designed biosynthetic pathways. Although suitable Streptomyces hosts exist, including genetically engineered cluster-free mutants, the approach is currently limited by the relative paucity of synthetic biology tools facilitating the de novo assembly of multicomponent gene clusters. Here, we report a modular system (MoClo) for Streptomyces including a set of adapted vectors and genetic elements, which allow for the construction of complete genetic circuits. Critical functional validation of each of the elements was obtained using the previously reported β-glucuronidase (GusA) reporter system. Furthermore, we provide proof-of-principle for the toolbox inS. albus, demonstrating the efficient assembly of a biosynthetic pathway to flavokermesic acid (FK), an advanced precursor of the commercially valuable carminic acid.

Viscosity and structure correlation mechanism of Fe[sbnd]Cr[sbnd]C melt based on MD simulation

The viscosity of FeCCr melt affects the mass transfer behavior during steelmaking processes. Reasonable regulation of viscosity can improve production efficiency. There is a close relationship between melt viscosity and structure. Therefore, it is of great theoretical significance to analyze the correlation between viscosity and structure of chromium-containing iron melt for the optimization of the whole metallurgical process. This study experimentally investigated the viscosity of FeCCr melt and utilized molecular dynamics simulation to analyze the formation and growth behavior of atomic clusters, revealing the correlation mechanism between structure and viscosity. As the Cr content increased from 11 % to 13 %, the melt viscosity increased by 10.05 %. At a temperature range from 1773 K to 1473 K, the viscosity reached a maximum of 15.5 mPa·s, representing a 40.9 % increase. Simulation results indicated that CrC clusters within the melt act as intermediaries, connecting with CrFe and FeC clusters to form FeCrC clusters. The key influence of increased Cr content on viscosity lies in promoting the generation of FeCrC multicomponent clusters, while the key influence of temperature reduction lies in promoting cluster growth. With the increase in Cr content, the number of clusters at 1773 K increased from 154 to 175, and the average volume of clusters increased from 212 Å3 to 224 Å3. When the temperature decreased from 1773 K to 1473 K, the number of clusters increased by 51.2 %, and the average volume of clusters increased by 18.75 %. In comparison, the thermal state change had a greater impact on clusters, where temperature reduction led to rapid cluster growth, reduction of free volume within the melt, and ultimately resulted in increased viscosity due to the combined effects.

Assessment of Anharmonicities in Clusters: Developing and Validating a Minimum-Information Partition Function.

Precise thermodynamic calculations are essential for understanding the dynamics of cluster systems and new particle formation. However, the widely employed harmonic statistical mechanical approach often falls short in terms of accuracy. In this study, we present an improved statistical model that incorporates vibrational anharmonicity via a novel partition function that requires only one additional system-specific input parameter. In addition to considering vibrational aspects, we also account for anharmonicity related to the configurational space. The role of anharmonicities is thoroughly examined in the case of general clusters, where the complete sets of conformers, mechanically stable spatial arrangements, are known up to clusters composed of 14 monomers. By performing consistent Monte Carlo simulations on these systems, we benchmark the statistical model's efficacy in reproducing key thermodynamic properties (formation free energy and potential energy) in the classical limit. The model exhibits exceptional alignment with simulations, accurately reproducing free energies within a precision of 2kBT and reliably capturing cluster melting temperatures. Furthermore, we demonstrate the significance and applicability of the model by reproducing thermodynamic barriers in homogeneous gas-phase nucleation of larger clusters. The transferability of our developed approach extends to more complex molecular systems and bears relevance for atmospheric multicomponent clusters, in particular.

Computational Tools for Handling Molecular Clusters: Configurational Sampling, Storage, Analysis, and Machine Learning.

Computational modeling of atmospheric molecular clusters requires a comprehensive understanding of their complex configurational spaces, interaction patterns, stabilities against fragmentation, and even dynamic behaviors. To address these needs, we introduce the Jammy Key framework, a collection of automated scripts that facilitate and streamline molecular cluster modeling workflows. Jammy Key handles file manipulations between varieties of integrated third-party programs. The framework is divided into three main functionalities: (1) Jammy Key for configurational sampling (JKCS) to perform systematic configurational sampling of molecular clusters, (2) Jammy Key for quantum chemistry (JKQC) to analyze commonly used quantum chemistry output files and facilitate database construction, handling, and analysis, and (3) Jammy Key for machine learning (JKML) to manage machine learning methods in optimizing molecular cluster modeling. This automation and machine learning utilization significantly reduces manual labor, greatly speeds up the search for molecular cluster configurations, and thus increases the number of systems that can be studied. Following the example of the Atmospheric Cluster Database (ACDB) of Elm (ACS Omega, 4, 10965-10984, 2019), the molecular clusters modeled in our group using the Jammy Key framework have been stored in an improved online GitHub repository named ACDB 2.0. In this work, we present the Jammy Key package alongside its assorted applications, which underline its versatility. Using several illustrative examples, we discuss how to choose appropriate combinations of methodologies for treating particular cluster types, including reactive, multicomponent, charged, or radical clusters, as well as clusters containing flexible or multiconformer monomers or heavy atoms. Finally, we present a detailed example of using the tools for atmospheric acid-base clusters.

Nitric Acid and Organic Acids Suppress the Role of Methanesulfonic Acid in Atmospheric New Particle Formation.

Multicomponent atmospheric molecular clusters, typically comprising a combination of acids and bases, play a pivotal role in our climate system and contribute to the perplexing uncertainties embedded in modern climate models. Our understanding of cluster formation is limited by the lack of studies on complex mixed-acid-mixed-base systems. Here, we investigate multicomponent clusters consisting of mixtures of several acid and base molecules: sulfuric acid (SA), methanesulfonic acid (MSA), nitric acid (NA), formic acid (FA), along with methylamine (MA), dimethylamine (DMA), and trimethylamine (TMA). We calculated the binding free energies of a comprehensive set of 252 mixed-acid-mixed-base clusters at the DLPNO-CCSD(T0)/aug-cc-pVTZ//ωB97X-D/6-31++G(d,p) level of theory. Combined with the existing datasets, we simulated the new particle formation (NPF) rates using the Atmospheric Cluster Dynamics Code (ACDC). We find that the presence of NA and FA had a substantial impact, increasing the NPF rate by 60% at realistic conditions. Intriguingly, we find that NA and FA suppress the role of MSA in NPF. These findings suggest that even high concentration of MSA has a limited impact on NPF in polluted regions with high FA and NA. We outline a method for generating a lookup table that could potentially be used in climate models that sufficiently incorporates all the required chemistry. By unraveling the molecular mechanisms of mixed-acid-mixed-base clusters, we get one step closer to comprehending their implications for our global climate system.

Insight into the Fe atom-FeS cluster synergistic catalysis mechanism for the oxygen evolution reaction in NiS2-based electrocatalysts.

The development of highly active oxygen evolution reaction (OER) catalysts with fast kinetics is crucial for the advancement of clean energy and fuel conversion to achieve a sustainable energy future. Recently, the synergistic effect of single-atom doping and multicomponent clusters has been demonstrated to significantly improve the catalytic activity of materials. However, such synergistic effects involving multi-electron and proton transfer processes are quite complex and many crucial mechanistic details need be well comprehended. We ingeniously propose a catalyst, (Fed-FeSc)@NiS2 (d stands for doping and c stands for clustering), with Fe and FeS acting synergistically on a NiS2 substrate. Specifically, fully dynamic monitoring of multiple active sites at the (Fed-FeSc)@NiS2 interface using metadynamics is innovatively performed. The results show that the rate determining step value at the overpotential of 1.23 V for the synergistic (Fed-FeSc)@NiS2 is 1.55 V, decreased by 6.67% and 35.29% compared to those of the independently acting single-atom doping and multi-clusters. The unique synergistic structure dramatically increases the d-band centre of the Fe site (-1.45 eV), endowing (Fed-FeSc)@NiS2 with more activity than conventional commercial Ir-C catalysts. This study provides insights into the synergistic effects of single-atom doping and multi-component clusters, leading to exploratory inspiration for the design of highly efficient OER catalysts.

Mass Balance Imaging: A Phase Portrait Analysis for Characterizing Growth Kinetics of Biomolecular Condensates.

Biomolecular condensation has emerged as a key organizing principle governing the formation of membraneless cellular assemblies. Revealing the mechanism of formation of biomolecular condensates requires the quantitative examination of their growth kinetics. Here, we introduce mass balance imaging (MBI) as a general method to study compositional growth dynamics based on fluorescent images of multicomponent clusters. MBI allows the visualization and measurement of composition-dependent growth rates of biomolecular condensates and other assemblies. We provide a computational pipeline and demonstrate the applicability of our method by investigating cortical assemblies containing N-WASP (WSP-1) and F-actin that appear during oocyte cortex activation in C. elegans. In general, the method can be broadly implemented to identify interactions that underlie growth kinetics of multicomponent assemblies in vivo and in vitro.

Long term stability of a high-entropy CoCrFeNiTi alloy fabricated by mechanical alloying

The stability of high-entropy alloys is of great importance for their fundamentals and practical applications. We experimentally studied the phase and microstructural evolution of near-equiatomic Co0.18Cr0.20Fe0.24Ni0.19Ti0.19 alloy during long-term (up to 204 days) isothermal annealing at 873, 1073, and 1273 K. The as-synthesized material fabricated by mechanical alloying was found to be metastable, i.e., after one day of annealing at 873 K, the concentration of Ti atoms in the multicomponent solid solution decreased by a factor of three. However, the resulting composition, Co0.22Cr0.23Fe0.29Ni0.20Ti0.06, persisted for more than 200 days of heat treatment and can be considered a stable phase. It consists of two FCC phases with essentially indistinguishable chemical compositions and similar crystal lattice parameters, i.e., 3.597 Å and 3.616 Å. The released Ti atoms work like an inner getter, extracting dissolved contaminating oxygen from the multicomponent phase and forming rounded TiOx sub-micron particles that may work as dispersion strengthening and hardening factors. Some plate-like Co0.2Ni0.5Ti0.3 intermetallic phases also precipitated. In addition to the phase transformations, coarsening of the microstructure continued throughout the annealing time. An ordered superstructure of rounded multicomponent clusters (∼ 0.5 nm) packed in a strained hexagonal structure with a periodicity of ∼1.1 nm was observed after annealing at 1273 K. The superlattice was formed as a result of the short-range ordering of the five-component matrix phase. Thus, after long-termed annealing high-entropy alloy Co-Cr-Fe-Ni-Ti attains stable, although not single-phase state.

An Improved Self-Adaptive Differential Evolution with the Neighborhood Search Algorithm for Global Optimization of Bimetallic Clusters.

Global optimization of multicomponent cluster structures is considerably time-consuming due to the existence of a vast number of isomers. In this work, we proposed an improved self-adaptive differential evolution with the neighborhood search (SaNSDE) algorithm and applied it to the global optimization of bimetallic cluster structures. The cross operation was optimized, and an improved basin hopping module was introduced to enhance the searching efficiency of SaNSDE optimization. Taking (PtNi)N (N = 38 or 55) bimetallic clusters as examples, their structures were predicted by using this algorithm. The traditional SaNSDE algorithm was carried out for comparison with the improved SaNSDE algorithm. For all the optimized clusters, the excess energy and the second difference of the energy were calculated to examine their relative stabilities. Meanwhile, the bond order parameters were adopted to quantitatively characterize the cluster structures. The results reveal that the improved SaNSDE algorithm possessed significantly higher searching capability and faster convergence speed than the traditional SaNSDE algorithm. Furthermore, the lowest-energy configurations of (PtNi)38 clusters could be classified as the truncated octahedral and disordered structures. In contrast, all the optimal (PtNi)55 clusters were approximately icosahedral. Our work fully demonstrates the high efficiency of the improved algorithm and advances the development of global optimization algorithms and the structural prediction of multicomponent clusters.

A consistent formation free energy definition for multicomponent clusters in quantum thermochemistry

Quantum chemical calculations have proven to describe the thermodynamics of sub-nanometre clusters more accurately than the liquid drop model used in classical nucleation theory (CNT). However, the standard quantum chemical free energies of multicomponent clusters are fundamentally incompatible with the quantity appearing in the exponential of the CNT expression for the nucleation rate. The origin of this incompatibility is known to be in the statistical thermochemistry, but it is also connected to a much debated issue of self-consistency within CNT. Although these issues do not affect the main results of previous theoretical studies of nucleation, a thorough analysis and discussion about the nature of free energy in quantum chemistry of cluster formation has been missing. In this study, we present a consistent definition for the formation free energy using the law of mass action and a general equilibrium cluster distribution function, and apply this definition to gas-phase quantum thermochemical calculations. This internal consistency allows us to integrate the high-level thermochemical data to the basic framework of CNT and the liquid drop model contained therein. Moreover, based on our analysis, we derive a simple analytical expression for the steady-state nucleation rate compatible with the widely used numerical acdc model. As an illustrative example, the consistent formation free energies and nucleation rates are presented and analysed for atmospherically relevant H2SO4–NH3 clusters at various temperature and monomer vapour pressure conditions.

Theoretical analysis of sulfuric acid–dimethylamine–oxalic acid–water clusters and implications for atmospheric cluster formation

In recent years, organic compounds potentially involved in atmospheric particle formation have received increased attention. However, the contributions of organic acids as precursors in nucleation remain ambiguous. In this study, the low-lying structures and thermodynamics of the sulfuric acid–dimethylamine–oxalic acid–water system are obtained at the M06-2X/6-311+G(2d,p) level, and the single point energy of the clusters has been calculated at the DF-LMP2-F12/VDZ-F12 level. The formations of the multicomponent clusters are predicted based on thermodynamics, involving proton transfer and hydrogen bonding interactions. Oxalic acid can synergistically promote the formation of the sulfuric acid–dimethylamine–oxalic acid–water system while inhibiting this with the addition of more sulfuric acid molecules. The results of hydrate distribution show that un-hydrate clusters play a dominant role during formation. Moreover, dimethylamine and oxalic acid have similar effects on Rayleigh scattering properties, and the clusters involving complex mixtures of compounds can have high optical activities.

Аналіз структурно семантичних характеристик термінів аграрного спрямування в перекладацькому аспекті

Abstract. The article summarizes the theoretical experience of studying the structural and semantic characteristics of scientific and technical terms and agricultural terms in particular. It has substantiated the fact that the agrarian sphere is experiencing a rapid emergence of terms-innovations that are quickly passing all stages of structural-semantic assimilation. A significant percentage of terms are formed as a result of rethinking their previous meanings, which leads to the emergence of so-called semantic terms. Analysis of structural and semantic features of agricultural terms showed that their formation occurs through derivation, terminologization of common vocabulary and assimilation of borrowings. The paper also analyzes examples of the functioning of English-language multicomponent clusters and the peculiarities of their translation. The purpose of research is to clarify the structural and semantic features of agricultural terminology, to identify and describe the processes of formation of one- and multi-component agricultural terms, to determine the main word-forming types of agricultural terminology. Results of research. Agrarian terminology, which is a set of words and stable phrases corresponding to the concepts functioning in this area (objects, phenomena and actions), is a mobile and flexible part of common vocabulary, and therefore requires systematization. The main carriers of these concepts are nouns, a certain number of verbs and adjectives which most serve as definitions in multicomponent clusters and are not independent. Word-forming means are mainly Latin-Greek morphemes, word stems and phrases. Depending on the participation of language in term formation, the terms of the agricultural sector are divided into three types: simple terms or word-terms; complex terms; terms-phrases or multicomponent clusters Agrarian terminology uses such ways of word formation as lexical-semantic, lexical-syntactic, morphological, abbreviation, and morphological-syntactic. Many commonly used lexical items acquire meanings inherent in the agricultural sector, becoming terms with a narrower meaning. Modern scientific literature on agricultural topics contains terms that have two or more meanings in this area, the clarification of which is only disclosed by the context. In translation, such ambiguity causes blurring of meaning, vagueness, substitution of terms and ultimately distortion of the content as a whole. It is important to consider the lexical and grammatical environment of the term. Due to the context, the following translation issues can be addressed: a) the word is used in its common or special narrow meaning; b) the choice of one of several meanings of a polysemous term in a particular context. From agrarian vocabulary, terminological units move into colloquial language, sometimes becoming jargons or terminoids that function in limited areas of engineering and technology, forming a layer of stylized new scientific and technical terms. Professional slang is usually short, expressive and to some extent stylistically colored. The most successful of them are fixed in the terminology system over time and receive official recognition in a certain subject area, being fixed in dictionaries, and some pass to other areas and into the common language. Specifics of functioning the terminological combinations require appropriate methods of their translation, among which there are a few main ones: literal translation of lexical units is carried out with the help of calque; replacement of parts of speech; explanatory translation of terms; translation with word order changes, primarily in attributive group. Conclusions. Agro-biological terminology tends to be poly-variant in translation, polysemy and homonymy. In order to overcome the difficulties in translating professional texts, it is necessary to work more actively with special vocabulary, thoroughly study the issues of ambiguity, synonyms and antonyms, word formation and methods of translation. The above considerations open opportunities for the practical application of these methods of translation of agro-biological terminology, and for further creative search for the correct perception of professionally oriented texts.

Mode merging for the finite mixture of t‐distributions

Finite mixture models can be interpreted as a model representing heterogeneous subpopulations within the whole population. However, more care is needed when associating a mixture component with a cluster, because a mixture model may fit more components than the number of clusters. Modal merging via the mean shift algorithm can help identify such multicomponent clusters. So far, most of the related works are focused on the Gaussian finite mixture. As the non‐Gaussian finite mixture models are gaining attention, the need to address the component‐cluster correspondence issue in these mixture models grows. Thus, we introduce a mode merging method via the mean shift for the finite mixture of t‐distributions and its parsimonious variants. It can be framed as an expectation–maximization algorithm and enjoys similar theoretical properties as the mean shift for the Gaussian finite mixture. The performance of our method is demonstrated via simulated and real data experiments, where it shows a competitive performance against some of the existing methods.

Virtual Model of Grip-and-Cut Picking for Simulation of Vibration and Falling of Grape Clusters

Abstract. Current robotic prototypes mainly harvest fruit clusters by gripping and cutting the main rachis. However, severe cluster vibration and fruit falling may occur during speedy harvesting. Determining the characteristics of this vibration and falling is important to achieve optimal control in robotic harvesting. Therefore, a virtual grip-and-cut model of cluster picking was developed, and corresponding cluster vibration and fruit falling simulations were performed for robotic harvesting. Multi-property finite element modeling of the main rachis was based on measurements of the rachis compression, shearing, and bending properties. A simulation model of cluster vibration was then built and combined with an end-effector model to obtain a virtual model of grip-and-cut cluster picking. The 3D vibration simulation model of a fruit cluster coupled with the grip-and-cut end-effector model effectively characterized the complex vibration in multilevel and multicomponent fruit clusters with individual diversity of the components’ properties. With the virtual model for cluster picking, dynamic interactive simulation can be realized of the fruit cluster and the grip-and-cut end-effector. Experiments verified that the average deviation of the average swing angle and fruit falling ratio were 6.09% and 8.71%, respectively. Furthermore, the finger-rachis deviation and grip-and-cut speed had a significant influence on the swing angle and fruit falling ratio. The 3D vibration simulation model of a fruit cluster and the virtual model of cluster picking are useful tools for virtual analysis and design of robotic harvesters. Keywords: Falling, Fruit cluster, Harvesting, Robot, Simulation, Vibration.

Method for Simultaneous Prediction of Atomic Structure and Stability of Nanoclusters in a Wide Area of Compositions.

We present a universal method for the large-scale prediction of the atomic structure of clusters. Our algorithm performs the joint evolutionary search for all clusters in a given area of the compositional space and takes advantage of structural similarities frequently observed in clusters of close compositions. The resulting speedup is up to 50 times compared to current methods. This enables first-principles studies of multicomponent clusters with full coverage of a wide range of compositions. As an example, we report an unprecedented first-principles global optimization of 315 Si nO m clusters with n ≤ 15 and m ≤ 20. The obtained map of Si-O cluster stability shows the existence of both expected (SiO2) n and unexpected (e.g., Si4O18) stable (magic) clusters, which can be important for a variety of applications.

An investigation about the structures, thermodynamics and kinetics of the formic acid involved molecular clusters

Despite the very important role of atmospheric aerosol nucleation in climate change and air quality, the detailed aerosol nucleation mechanism is still unclear. Here we investigated the formic acid (FA) involved multicomponent nucleation molecular clusters including sulfuric acid (SA), dimethylamine (DMA) and water (W) through a quantum chemical method. The thermodynamics and kinetics analysis was based on the global minima given by Basin-Hopping (BH) algorithm coupled with Density Functional Theory (DFT) and subsequent benchmarked calculations. Then the interaction analysis based on ElectroStatic Potential (ESP), Topological and Atomic Charges analysis was made to characterize the binding features of the clusters. The results show that FA binds weakly with the other molecules in the cluster while W binds more weakly. Further kinetic analysis about the time evolution of the clusters show that even though the formic acid’s weak interaction with other nucleation precursors, its effect on sulfuric acid dimer steady state concentration cannot be neglected due to its high concentration in the atmosphere.

Computational Studies on the ScnNm (n + m=10) Clusters: Structure, Electronic and Vibrational Properties

Besides the size and structure, compositions also dramatically affect the properties of clusters. In fact, the increased degree of freedom poses much more challenges to determine the global minimum structure of multi-component clusters. In this thesis, based on the CALYPSO structure searching method, the global minimum structures are obtained for ScnNm (n + m=10) clusters at PW91/6-311+G(d) level. The growth behavior indicates that the cage unit tends to arrange into the compact configurations, and the occupied positions of N atoms shift from the surface towards the center of coordination site with the increasing number of Sc atoms. The relative stabilities have been discussed by analyzing the average binding energies and HOMO–LUMO gaps. In addition, the molecular orbitals, dipole moments, polarizability, hyperpolarizabilities, natural population, natural electron configuration, and Infared and Raman spectra calculations allow complete characterization of the electronic and vibrational properties for the global minimum structural clusters.

Importance of Steric Influences in the Construction of Multicomponent Hybrid Polymetallic Clusters.

The straightforward room temperature synthesis of hybrid polymetallic manganese clusters is investigated, exploiting complementary ligand combinations of p-tert-butylcalix[4]arene and salicylaldoximes. Eight new [MnIII7MnII] clusters have been prepared wherein the simple substitution of alkyl or aryl groups at well-defined positions of the salicylaldoxime scaffold leads to two distinct structure types that, while exhibiting the same general topology, contain the unique MnII ion in different positions. Incorporation of a methyl, ethyl, or isopropyl group at the 3-position of the aromatic skeleton or a phenyl group at the oximic carbon gives structure type A that displays competing weak ferromagnetic and antiferromagnetic interactions. Substitution of a methyl or ethyl group at the oximic carbon atom invokes structure type B, incorporating an additional bulky chloride or nitrate into the metallic core due to the smaller steric imposition and position of the methyl or ethyl group. The distortion of the cluster core is consequently enhanced, switching the magnetic properties and resulting in single-molecule magnet behavior. The presence of tert-butyl groups at the 3- and 5-positions of the salicylaldoxime skeleton leads to a new [MnIV2MnIII2] cluster that is found to be a single-molecule magnet. The bulky tert-butyl group in the 3-position is too large to facilitate Mn8 cluster formation, and thus assembly occurs by an alternative pathway. Characteristic bonding modes of the constituent ligands are retained in every case, and the results presented here give insight into the potential of ligand combinations in future studies, highlighting the importance of steric factors in evaluating their relevant compatibilities.

Proton Transfer in Mixed Clusters of Methanesulfonic Acid, Methylamine, and Oxalic Acid: Implications for Atmospheric Particle Formation.

Understanding the properties of atmospheric particles made of several components is a very challenging problem. In this paper, we perform quantum chemical calculations for small multicomponent clusters of atmospheric relevance that incorporate methanesulfonic acid (MSA), methylamine (MA), oxalic acid (OxA), and water (H2O). Potential correlations between theoretical predictions of proton transfer in the small clusters and findings of recent experiments on formation of particles of detectable sizes (>2 nm) from the same components are studied. It is proposed that proton transfer from the acid to the amine in the 1:1 clusters correlates with experiments on particle formation in systems, such as MSA-MA and MSA-MA-OxA. In the case of OxA + MA, which has been observed to give few particles, proton transfer does not occur for the 1:1 cluster but does for the 2:2 cluster. Adding H2O to OxA-MA promotes the occurrence of proton transfer, and corresponding particles are slightly enhanced. The partial charge on the MA component increases by adding OxA or H2O to MSA-MA, which is correlated with enhanced particle formation compared to MSA-MA alone. Ab initio molecular dynamics simulations show that proton transfer at room temperature (T = 298 K) and high temperature (T = 500 K) is little affected compared with the equilibrium structure (T = 0 K). These results suggest that small cluster calculations may be useful in predicting the formation of multicomponent particles in the atmosphere.

Giant Hollow Heterometallic Polyoxoniobates with Sodalite‐Type Lanthanide–Tungsten–Oxide Cages: Discrete Nanoclusters and Extended Frameworks

AbstractThe first series of niobium–tungsten–lanthanide (Nb‐W‐Ln) heterometallic polyoxometalates {Ln12W12O36(H2O)24(Nb6O19)12} (Ln=Y, La, Sm, Eu, Yb) have been obtained, which are comprised of giant cluster‐in‐cluster‐like ({Ln12W12}‐in‐{Nb72}) structures built from 12 hexaniobate {Nb6O19} clusters gathered together by a rare 24‐nuclearity sodalite‐type heterometal–oxide cage {Ln12W12O36(H2O)24}. The Nb‐W‐Ln clusters present the largest multi‐metal polyoxoniobates and a series of rare high‐nuclearity 4d‐5d‐4f multicomponent clusters. Furthermore, the giant Nb‐W‐Ln clusters may be isolated as discrete inorganic alkali salts and can be used as building blocks to form high‐dimensional inorganic–organic hybrid frameworks.