Intrinsic-strain-induced ferroelectric order and ultrafine nanodomains in SrTiO3.

Nano ferroelectrics holds the potential application promise in information storage, electro-mechanical transformation, and novel catalysts but encounters a huge challenge of size limitation and manufacture complexity on the creation of long-range ferroelectric ordering. Herein, as an incipient ferroelectric, nanosized SrTiO3 was indued with polarized ordering at room temperature from the nonpolar cubic structure, driven by the intrinsic three-dimensional (3D) tensile strain. The ferroelectric behavior can be confirmed by piezoelectric force microscopy and the ferroelectric TO1 soft mode was verified with the temperature stability to 500 K. Its structural origin comes from the off-center shift of Ti atom to oxygen octahedron and forms the ultrafine head-to-tail connected 90° nanodomains about 2 to 3 nm, resulting in an overall spontaneous polarization toward the short edges of nanoparticles. According to the density functional theory calculations and phase-field simulations, the 3D strain-related dipole displacement transformed from [001] to [111] and segmentation effect on the ferroelectric domain were further proved. The topological ferroelectric order induced by intrinsic 3D tensile strain shows a unique approach to get over the nanosized limitation in nanodevices and construct the strong strain-polarization coupling, paving the way for the design of high-performance and free-assembled ferroelectric devices.

Decoding Active Sites for Highly Efficient Semihydrogenation of Acetylene in Palladium-Copper Nanoalloys.

Accurately decoding the three-dimensional atomic structure of surface active sites is essential yet challenging for a rational catalyst design. Here, we used comprehensive techniques combining the pair distribution function and reverse Monte Carlo simulation to reveal the surficial distribution of Pd active sites and adjacent coordination environment in palladium-copper nanoalloys. After the fine-tuning of the atomic arrangement, excellent catalytic performance with 98% ethylene selectivity at complete acetylene conversion was obtained in the Pd34Cu66 nanocatalysts, outperforming most of the reported advanced catalysts. The quantitative deciphering shows a large number of active sites with a Pd-Pd coordination number of 3 distributed on the surface of Pd34Cu66 nanoalloys, which play a decisive role in highly efficient semihydrogenation. This finding not only opens the way for guiding the precise design of bimetal nanocatalysts from atomic-level insight but also provides a method to resolve the spatial structure of active sites.

Enabling Quasi-Zero-Strain Behavior of Layered Oxide Cathodes via Multiple-cations Induced Order-to-Disorder Transition.

The chemically pre-intercalated lattice engineering is widely applied to elevate the electronic conductivity, expand the interlayer spacing, and improve the structural stability of layered oxide cathodes. However, the mainstream unitary metal ion pre-intercalation generally produces the cation/vacancy ordered superstructure, which astricts the further improvement of lattice respiration and charge-carrier ion storage and diffusion. Herein, a multiple metal ions pre-intercalation lattice engineering is proposed to break the cation/vacancy ordered superstructure. Taking the bilayer V2O5 as an example, Ni, Co, and Zn ternary ions are simultaneously pre-intercalated into its interlayer space (NiCoZnVO). It is revealed that the Ni─Co neighboring characteristic caused by Ni(3d)-O(2p)-Co(3d) orbital coupling and the Co-Zn/Ni-Zn repulsion effect due to chemical bond incompatibility, endow the NiCoZnVO sample with the cation/vacancy disordered structure. This not only reduces the Li+ diffusion barrier, but also increases the diffusion dimension of Li+ (from one-dimension to two-dimension). Particularly, Ni, Co, and Zn ions co-pre-intercalation causes a prestress, which realizes a quasi-zero-strain structure at high-voltage window upon charging/discharging process. The functions of Ni ion stabilizing the lattice structure and Co or Zn ions activating more Li+ reversible storage reaction of V5+/V4+ are further revealed. The cation/vacancy disordered structure significantly enhances Li+ storage properties of NiCoZnVO cathode.

Synchrotron Radiation Based X-ray Absorption Spectroscopy: Fundamentals and Applications in Photocatalysis.

Photocatalysis is one of the most promising green technologies to utilize solar energy for clean energy achievement and environmental governance. There is a knotty problem to rational designing high-performance photocatalyst, which largely depends on an in-depth insight into their structure-activity relationships and complex photocatalytic reaction mechanisms. Synchrotron radiation based X-ray absorption spectroscopy (XAS) is an important characterization method for photocatlayst to offer the element-specific key geometric and electronic structural information at the atomic level, on this basis, time-resolved XAS technique has a huge impact on mechanistic understanding of photochemical reaction owing to their powerful ability to probe, in real-time, the electronic and geometric structures evolution within photocatalysis reactions. This review will focus on the fundamentals of XAS and their applications in photocatalysis. The detailed applications obtained from XAS is described through the following aspects: 1) identifying local structure of photocatalyst; 2) uncovering in situ structure and chemical state evolution during photocatalysis; 3) revealing the photoexcited process. We will provide an in depth understanding on how the XAS method can guide the rational design of highly efficient photocatalyst. Finally, a systematic summary of XAS and related significance is made and the research perspectives are suggested.

Theoretical studies on the local structure and spin Hamiltonian parameters for Cu2+ ions in LiTaO3 crystal.

The local structure and spin Hamiltonian parameters (SHPs) g factors (gx , gy , gz ) and the hyperfine structure constants (Ax , Ay , Az ) for Cu2+ doped in the LiTaO3 crystal are theoretically investigated by the perturbation formulas for a 3d9 ion under rhombically elongated octahedral based on the cluster approach. The impurity Cu2+ was assumed to occupy the host trigonally-distorted octahedral Li+ site and experience the Jahn-Teller (JT) distortion from the host trigonal octahedral [TaO6 ]10- to the impurity rhombically elongated octahedral [CuO6 ]10- . Based on the calculations, the impurity-ligand bond lengths parallel and perpendicular to the C2 -axis are found to be R|| (≈ 2.305 Å) and R⊥ (≈ 2.112 Å) for the studied [CuO6 ]10- cluster, with the planar bond angle θ (≈ 78.2°). Meanwhile, the ground-state wave function for Cu2+ center in LiTaO3 was also obtained. The calculated SHPs based on the above local lattice distortions agree well with the experimental data, and the results are discussed.

Learning Causes of Functional Dynamic Targets: Screening and Local Methods.

This paper addresses the challenge of identifying causes for functional dynamic targets, which are functions of various variables over time. We develop screening and local learning methods to learn the direct causes of the target, as well as all indirect causes up to a given distance. We first discuss the modeling of the functional dynamic target. Then, we propose a screening method to select the variables that are significantly correlated with the target. On this basis, we introduce an algorithm that combines screening and structural learning techniques to uncover the causal structure among the target and its causes. To tackle the distance effect, where long causal paths weaken correlation, we propose a local method to discover the direct causes of the target in these significant variables and further sequentially find all indirect causes up to a given distance. We show theoretically that our proposed methods can learn the causes correctly under some regular assumptions. Experiments based on synthetic data also show that the proposed methods perform well in learning the causes of the target.

Preparation of Nanoparticles and Diffusion Behavior of Metal Ions in Ionic Liquids.

Ionic liquids (ILs) have attracted much attention as tunable liquids because of their unique structures and properties. However, the mechanisms of chemical reactions and solute diffusion in ionic liquids are still unknown. This article summarizes our previous studies and recent results on the mechanisms of metal particle formation and solute diffusion in ionic liquids, focusing on the local structure of ionic liquids. It was found that the shape and size of metal particles formed in ionic liquids using electron beams or X-rays are strongly influenced by the local structure. In the study of the diffusion behavior of metal ions in ionic liquids, we proposed a hopping-like diffusion model and proposed that this behavior could be strongly influenced by local structures such as hole concentration and/or domain structures.

Highly-efficient molten NaOH-KOH for organochlorine destruction: Performance and mechanism.

Molten salt has been increasingly acknowledged to be useful in the destruction of chlorine-containing organic wastes (COWs), e.g., organochlorine. However, the operational temperatures are usually high, and local structure and thermodynamic property of the molten salt remain largely unclear. In this study, novel molten NaOH-KOH is developed for organochlorine destruction, and its eutectic point can be lowered to 453 K with 1:1 mol ratio of NaOH to KOH. Further experiment shows that this molten NaOH-KOH is highly-efficient towards the destructions of both trichlorobenzene and dichlorophenol, acquiring the final dechlorination efficiencies as 88.2% and 94.1%, respectively. The organochlorine destruction and chloride salt enrichment are verified by fourier-transform infrared spectrometer. Molten NaOH-KOH not only eliminates the C-Cl and CC bonds, but also traps generated CO2, other acidic gases, and possibly particulate matters as a result of the high surface area and high viscosity. This makes it possibly advantageous over incineration for organic waste destruction for carbon neutrality. To sufficiently reveal the inherent mechanism for the temperature dependent performance, molecular dynamics simulation is further adopted. Results show that the radial distance between ions increases with temperature, causing larger molar volume and lower resistance to shear deformation. Moreover, thermal expansion coefficient, specific heat capacity, and ion self-diffusion coefficient of the molten NaOH-KOH are found to increase linearly with temperature. All these microscopic alterations contribute to the organochlorine destruction. This study benefits to develop highly-efficient molten system for COWs treatment via a low-carbon approach.

Local lattice distortions and dynamics in extremely overdoped superconducting YSr2Cu2.75Mo0.25O7.54.

A common characteristic of many "overdoped" cuprates prepared with high-pressure oxygen is Tc values ≥ 50 K that often exceed that of optimally doped parent compounds, despite O stoichiometries that place the materials at the edge or outside of the conventional boundary between superconducting and normal Fermi liquid states. X-ray absorption fine-structure (XAFS) measurements at 52 K on samples of high-pressure oxygen (HPO) YSr2Cu2.75Mo0.25O7.54, Tc = 84 K show that the Mo is in the (VI) valence in an unusually undistorted octahedral geometry with predominantly Mo neighbors that is consistent with its assigned substitution for Cu in the chain sites of the structure. Perturbations of the Cu environments are minimal, although the Cu X-ray absorption near-edge structure (XANES) differs from that in other cuprates. The primary deviation from the crystal structure is therefore nanophase separation into Mo- and Cu-enriched domains. There are, however, indications that the dynamical attributes of the structure are altered relative to YBa2Cu3O7, including a shift of the Cu-apical O two-site distribution from the chain to the plane Cu sites. Another effect that would influence Tc is the possibility of multiple bands at the Fermi surface caused by the presence of the second phase and the lowering of the Fermi level.

Investigations on the local structure and the spin Hamiltonian parameters for Cu2+ in xCuO-(68-x)V2 O5 -32TeO2 glasses.

The defect structure, spin Hamiltonian parameters (SHPs: anisotropic g factors g ‖ and g ⊥ and the hyperfine structure constants A ‖ and A ⊥ ), and their compositional dependence of Cu 2 + in x CuO - ( 68 - x ) V 2 O 5 - 32 TeO 2 ( x = 5, 10, 20, 30 mol%) glasses are quantitatively analyzed by using the higher-order perturbation formula of octahedral complex with tetrahedral elongation distortion. Due to the Jahn-Teller effect, the [ CuO 6 ] 10 - group is subjected to tetragonal elongation distortion of varying degrees. D q , N , ρ , κ , and H show nonlinear changes with the concentrations of Cu 2 + . When x = 10 mol% CuO, the degree of distortion ( ρ ≈ 0 . 1 % ) is the smallest; when x = 30 mol% CuO, the degree of distortion ( ρ ≈ 15 % ) is the largest, which indicates that excessive distortion leads to the appearance of Z -axis oxygen vacancies and the coordination number of copper ions from six to four. The increasing tendency of the evaluated N and H reveals decreasing covalency of the whole glass system. Present theoretical studies would be useful to the explore the structural properties and optical applications of glass with different CuO concentrations.

The Role of Zn Ions in the Structural, Surface, and Gas-Sensing Properties of SnO2:Zn Nanocrystals Synthesized via a Microwave-Assisted Route.

Although semiconducting metal oxide (SMOx) nanoparticles (NPs) have attracted attention as sensing materials, the methodologies available to synthesize them with desirable properties are quite limited and/or often require relatively high energy consumption. Thus, we report herein the processing of Zn-doped SnO2 NPs via a microwave-assisted nonaqueous route at a relatively low temperature (160 °C) and with a short treatment time (20 min). In addition, the effects of adding Zn in the structural, electronic, and gas-sensing properties of SnO2 NPs were investigated. X-ray diffraction and high-resolution transmission electron microscopy analyses revealed the single-phase of rutile SnO2, with an average crystal size of 7 nm. X-ray absorption near edge spectroscopy measurements revealed the homogenous incorporation of Zn ions into the SnO2 network. Gas sensing tests showed that Zn-doped SnO2 NPs were highly sensitive to sub-ppm levels of NO2 gas at 150 °C, with good recovery and stability even under ambient moisture. We observed an increase in the response of the Zn-doped sample of up to 100 times compared to the pristine one. This enhancement in the gas-sensing performance was linked to the Zn ions that provided more surface oxygen defects acting as active sites for the NO2 adsorption on the sensing material.

Effective Local and Secondary Protein Structure Prediction by Combining a Neural Network-Based Approach with Extensive Feature Design and Selection without Reliance on Evolutionary Information.

Protein structure prediction continues to pose multiple challenges despite outstanding progress that is largely attributable to the use of novel machine learning techniques. One of the widely used representations of local 3D structure-protein blocks (PBs)-can be treated in a similar way to secondary structure classes. Here, we present a new approach for predicting local conformation in terms of PB classes solely from amino acid sequences. We apply the RMSD metric to ensure unambiguous future 3D protein structure recovery. The selection of statistically assessed features is a key component of the proposed method. We suggest that ML input features should be created from the statistically significant predictors that are derived from the amino acids' physicochemical properties and the resolved structures' statistics. The statistical significance of the suggested features was assessed using a stepwise regression analysis that permitted the evaluation of the contribution and statistical significance of each predictor. We used the set of 380 statistically significant predictors as a learning model for the regression neural network that was trained using the PISCES30 dataset. When using the same dataset and metrics for benchmarking, our method outperformed all other methods reported in the literature for the CB513 nonredundant dataset (for the PBs, Q16 = 81.01%, and for the DSSP, Q3 = 85.99% and Q8 = 79.35%).

Core Promoter Regions of Antisense and Long Intergenic Non-Coding RNAs.

RNA polymerase II (POL II) is responsible for the transcription of messenger RNAs (mRNAs) and long non-coding RNAs (lncRNAs). Previously, we have shown the evolutionary invariance of the structural features of DNA in the POL II core promoters of the precursors of mRNAs. In this work, we have analyzed the POL II core promoters of the precursors of lncRNAs in Homo sapiens and Mus musculus genomes. Structural analysis of nucleotide sequences in positions -50, +30 bp in relation to the TSS have shown the extremely heterogeneous 3D structure that includes two singular regions - hexanucleotide "INR" around the TSS and octanucleotide "TATA-box" at around ~-28 bp upstream. Thus, the 3D structure of core promoters of lncRNA resembles the architecture of the core promoters of mRNAs; however, textual analysis revealed differences between promoters of lncRNAs and promoters of mRNAs, which lies in their textual characteristics; namely, the informational entropy at each position of the nucleotide text of lncRNA core promoters (by the exception of singular regions) is significantly higher than that of the mRNA core promoters. Another distinguishing feature of lncRNA is the extremely rare occurrence in the TATA box of octanucleotides with the consensus sequence. These textual differences can significantly affect the efficiency of the transcription of lncRNAs.

[Method to Generate Complex Predictive Features for Machine Learning-Based Prediction of the Local Structure and Functions of Proteins].

Recently, prediction of the structure and function of a protein from its sequence underwent a rapid increase in performance. It is primarily due to the application of machine learning methods, many of which rely on the predictive features supplied to them. It is thus crucial to retrieve the information encoded in the amino acid sequence of a protein. Here we propose a method to generate a set of complex yet interpretable predictors, which aids in revealing factors that influence protein conformation. The method makes it possible to generate predictive features and test them for significance both in the context of a general description of the protein structures and functions and in the context of highly specific predictive tasks. Having generated an exhaustive set of predictors, we narrow it down to a smaller curated set of informative features using feature selection methods, which increases the performance of subsequent predictive modelling. We illustrate the efficiency of our methodology by applying it to local protein structure prediction, where the rate of correct prediction for DSSP Q3 (three-class classification) is 81.3%. The method is implemented in C++ for command line use and can be run on any operating system. The source code is released on GitHub at https://github.com/Milchevskiy/protein-encoding-projects.

Persistent Structure and Frustrated Magnetism in High Entropy Rare-Earth Zirconates.

The configurational complexity and distinct local atomic environments of high entropy oxides remain largely unexplored, leaving structure-property relationships and the hypothesis that the family offers rich tunability for applications ambiguous. This work investigates the influence of cation size and materials synthesis in determining the resulting structure and magnetic properties of a family of high entropy rare-earth zirconates (HEREZs, nominal composition RE2 Zr2 O7 with RE = rare-earth element combinations including Eu, Gd, Tb, Dy, Ho, La, or Sc). The structural characterization of the series is examined through synchrotron X-ray diffraction and pair distribution function analysis, and electron microscopy, demonstrating average defect-fluorite structures with considerable local disorder, in all samples. The surface morphology and particle sizes are found to vary significantly with preparation method, with irregular micron-sized particles formed by high temperature sintering routes, spherical nanoparticles resulting from chemical co-precipitation methods, and porous nanoparticle agglomerates resulting from polymer steric entrapment synthesis. In agreement with the disordered cation distribution found across all samples, magnetic measurements indicate that all synthesized HEREZs show frustrated magnetic behavior, as seen in a number of single-component RE2 Zr2 O7 pyrochlore oxides. These findings advance the understanding of the local structure of high entropy oxides and demonstrate strategies for designing nanostructured morphologies in the class.

A flexible method for parameterizing ranked nodes in Bayesian networks using Beta distributions.

When novice modelers first attempt to build a Bayesian network, they are often impressed with the intuitive graphical structures that capture their causal understanding. This favorable impression evaporates on proceeding to parameterization. Conditional probability tables (CPT) require parameters for often hundreds of very similar scenarios and specifying them in the absence of data can be overwhelming. The problem is even more severe when eliciting parameters from experts with limited time. Often, there is local structure with fewer parameters that better describes the relationship. Such structures include the Noisy OR, decision trees, and equations. These work well for modelers, but can be an issue for experts and particularly groups of experts. An alternative approach is to elicit only a few CPT rows and interpolate the remainder. This is a promising approach, as it can handle unknown structures and multiple experts, but existing techniques can be limited. Here, we present a flexible approach called InterBeta for performing CPT interpolation with ordered nodes. In the simplest case, just two CPT rows are needed, but this can be easily augmented with further information. The basic approach assumes input independence, but allows dependencies to be reintroduced as required, and can also be combined with other local structures such as decision trees or equations, leaving the interpolator to fill in the gaps. We explain the InterBeta method, describe its capabilities and limitations and how it compares to similar approaches and show how it can trade-off elicitation effort against faithfully representing expert understanding.

An investigation of the role of transition metal ions impurities in CdO: Local structure and electronic properties.

According to the high-order perturbation formulae of 3d5 (Mn2+ ) and 3d9 (Cu2+ ) ions in octahedron, the local structures and electron paramagnetic resonance (EPR) parameters (g factors and hyperfine structure constants A) for Cu2+ and Mn2+ in CdO are theoretically studied in a consistent way. Due to the Jahn-Teller effect, both the substituted sites of Cu2+ and Mn2+ show the tetragonally elongated distortion with different elongation τ. Meanwhile, the crystal field and covalency around doped Cu2+ and Mn2+ are obtained, which can account for the electronic properties in doped CdO. In order to make further investigation of the potential optical and electrical properties, the band structure and density of states (DOS) of pure and transition metal ions (TMs) doped CdO are comparably calculated through density functional theory (DFT). The results show that the band gap of Mn2+ - and Cu2+ -doped CdO can be effectively reduced, due to the improved covalency between the central ions and ligand ions.

Spontaneous Strain Buffer Enables Superior Cycling Stability in Single-Crystal Nickel-Rich NCM Cathode.

The capacity degredation in layered Ni-rich LiNixCoyMnzO2 (x ≥ 0.8) cathode largely originated from drastic surface reactions and intergranular cracks in polycrystalline particles. Herein, we report a highly stable single-crystal LiNi0.83Co0.12Mn0.05O2 cathode material, which can deliver a high specific capacity (∼209 mAh g-1 at 0.1 C, 2.8-4.3 V) and meanwhile display excellent cycling stability (>96% retention for 100 cycles and >93% for 200 cycles). By a combination of in situ X-ray diffraction and in situ pair distribution function analysis, an intermediate monoclinic distortion and irregular H3 stack are revealed in the single crystals upon charging-discharging processes. These structural changes might be driven by unique Li-intercalation kinetics in single crystals, which enables an additional strain buffer to reduce the cracks and thereby ensure the high cycling stability.

Evolutionary Invariant of the Structure of DNA Double Helix in RNAP II Core Promoters.

Eukaryotic and archaeal RNA polymerase II (POL II) machinery is highly conserved, regardless of the extreme changes in promoter sequences in different organisms. The goal of our work is to find the cause of this conservatism. The representative sets of aligned promoter sequences of fifteen organisms belonging to different evolutional stages were studied. Their textual profiles, as well as profiles of the indexes that characterize the secondary structure and the mechanical and physicochemical properties, were analyzed. The evolutionarily stable, extremely heterogeneous special secondary structure of POL II core promoters was revealed, which includes two singular regions-hexanucleotide "INR" around TSS and octanucleotide "TATA element" of about -28 bp upstream. Such structures may have developed at some stage of evolution. It turned out to be so well matched for the pre-initiation complex formation and the subsequent initiation of transcription for POL II machinery that in the course of evolution there were selected only those nucleotide sequences that were able to reproduce these structural properties. The individual features of specific sequences representing the singular region of the promoter of each gene can affect the kinetics of DNA-protein complex formation and facilitate strand separation in double-stranded DNA at the TSS position.

Deep Link-Prediction Based on the Local Structure of Bipartite Networks.

Link prediction based on bipartite networks can not only mine hidden relationships between different types of nodes, but also reveal the inherent law of network evolution. Existing bipartite network link prediction is mainly based on the global structure that cannot analyze the role of the local structure in link prediction. To tackle this problem, this paper proposes a deep link-prediction (DLP) method by leveraging the local structure of bipartite networks. The method first extracts the local structure between target nodes and observes structural information between nodes from a local perspective. Then, representation learning of the local structure is performed on the basis of the graph neural network to extract latent features between target nodes. Lastly, a deep-link prediction model is trained on the basis of latent features between target nodes to achieve link prediction. Experimental results on five datasets showed that DLP achieved significant improvement over existing state-of-the-art link prediction methods. In addition, this paper analyzes the relationship between local structure and link prediction, confirming the effectiveness of a local structure in link prediction.