Local Structure Research Articles

Coordination Desymmetrization of Copper Single-Atom Catalyst for Efficient Nitrate Reduction.

Coordination engineering strategy for optimizing the catalytic performance of single-atom catalysts (SACs) has been rapidly developed over the last decade. However, previous reports on copper SACs for nitrate reduction reactions (NO3RR) have mostly focused on symmetric coordination configurations such as Cu-N4 and Cu-N3. In addition, the mechanism in terms of the regulation of coordination environment and catalytic properties of SACs has not been well demonstrated. Herein, we disrupted the local symmetric structure of copper atoms by introducing unsaturated heteroatomic coordination of Cu-O and Cu-N to achieve the coordination desymmetrization of Cu-N1O2 SACs. The Cu-N1O2 SACs exhibit an efficient nitrate-to-ammonia conversion with a high FE of ~96.5 % and a yield rate of 3120 μg NH3 h-1 cm-2 at -0.60 V vs RHE. As indicated by in situ Raman spectra, the catalysts facilitate the accumulation of NO3 - and the selective adsorption of *NO2, which were further confirmed by the theoretical study of surface dipole moment and orbital hybridization. Our work illustrated the correlation between the coordination desymmetrization and the catalytic performance of copper SACs for NO3RR.

Correlating local structure and migration dynamics in Na/Li dual ion conductor Na5YSi4O12

Na5YSi4O12 (NYSO) is demonstrated as a promising electrolyte with high ionic conductivity and low activation energy for practical use in solid Na-ion batteries. Solid-state NMR was employed to identify the six types of coordination of Na+ ions and migration pathway, which is vital to master working mechanism and enhance performance. The assignment of each sodium site is clearly determined from high-quality 23Na NMR spectra by the aid of Density Functional Theory calculation. Well-resolved 23Na exchangespectroscopy and electrochemical tracer exchange spectra provide the first experimental evidence to show the existence of ionic exchange between sodium at Na5 and Na6 sites, revealing that Na transport route is possibly along three-dimensional chain of open channel-Na4-open channel. Variable-temperature NMR relaxometry is developed to evaluate Na jump rates and self-diffusion coefficient to probe the sodium-ion dynamics in NYSO. Furthermore, NYSO works well as a dual ion conductor in Na and Li metal batteries with Na3V2(PO4)3 and LiFePO4 as cathodes, respectively.

Coupled Ni─Co Dual-Atom Catalyst for Guiding Sulfur and Lithium Evolutions in Lithium-Sulfur Batteries.

The kinetically retarded sulfur evolution reactions and notorious lithium dendrites as the major obstacles hamper the practical implementation of lithium-sulfur batteries (LSBs). Dual metal atom catalysts as a new model are expected to show higher activity by their rational coupling. Herein, the dual-atom catalyst with coupled Ni─Co atom pairs (Ni/Co-DAC) is designed successfully by programmed approaches. The Ni─Co atom pairs alter the local electron structure and optimize the coordination configuration of Ni/Co-DAC, leading to the coupling effect for promoting the interconversion of sulfur and guiding lithium plating/striping. The LSB delivers a remarkable capacity of 818mA h g-1 at 3.0 C and a low degeneration rate of 0.053% per cycle over 500 cycles. Moreover, the LSB with a high sulfur mass loading of 6.1mg cm-2 and lean electrolyte dosage of 6.0µL mgS -1 shows a remarkable areal capacity of 5.7mA h cm-2.

Continuous polyamorphic transition in high-entropy metallic glass

Polyamorphic transition (PT) is a compelling and pivotal physical phenomenon in the field of glass and materials science. Understanding this transition is of scientific and technological significance, as it offers an important pathway for effectively tuning the structure and property of glasses. In contrast to the PT observed in conventional metallic glasses (MGs), which typically exhibit a pronounced first-order nature, herein we report a continuous PT (CPT) without first-order characteristics in high-entropy MGs (HEMGs) upon heating. This CPT behavior is featured by the continuous structural evolution at the atomic level and an increasing chemical concentration gradient with temperature, but no abrupt reduction in volume and energy. The continuous transformation is associated with the absence of local favorable structures and chemical heterogeneity caused by the high configurational entropy, which limits the distance and frequency of atomic diffusion. As a result of the CPT, numerous glass states can be generated, which provides an opportunity to understand the nature, atomic packing, formability, and properties of MGs. Moreover, this discovery highlights the implication of configurational entropy in exploring polyamorphic glasses with an identical composition but highly tunable structures and properties.

Water structures in tip-charged carbon nanotubes.

Carbon nanotubes (CNTs) have potential applications in separation membranes and nanofluidic devices. It is well known that the behavior of water molecules confined in CNTs is affected by surface functional groups and external electric fields, leading to structural changes. The understanding of these structural changes of water within various CNTs is crucial, particularly in the context of material separation. While there have been many investigations into the effects of individual specific functional groups, a comprehensive understanding of the effect of these functional groups and the electric fields they generate on water molecules remains elusive. In this study, we investigate the properties of water molecules in tip-charged CNTs of (8,8), (10,10), and (12,12) chiral vectors with positive charges at one tip and negative charges at the other tip. Abstraction of ionized functional groups as tip charges enables a comprehensive understanding that is independent of individual functional groups. The symmetrically arranged tip-charges spontaneously generate a strong and symmetric electric field in the CNTs. However, the strength and directionality of the electric field are non-uniform and complex. In the interiors of (8,8) and (10,10) tip-charged CNTs, helical and square structures, which have disturbances caused by the non-uniformity of the electric field, are observed. The properties of the water molecules differed significantly in the center of the CNTs and near positive and negative charges, despite the electric field symmetry. In (12,12) tip-charged CNTs with 12 charges, a local ring structure is observed in the vicinity of negative charges but not in the vicinity of positive charges. It is concluded that the water structures in tip-charged CNTs have different characteristics from those in plain CNTs under a uniform electric field.

Tuning Valence Tautomerism in Dinuclear Cobalt Complexes by Modulating Communication in the Bridging Ligand

AbstractThe capability of bis(dioxolene) ligands to access multiple redox states makes them ideal candidates to tune the electronic properties of metal complexes, for example to achieve valence tautomerism (VT). In this study, a family of dinuclear cobalt complexes have been isolated with the bridging bis(dioxolene) thean− ligand in the cat2−‐cat2−, cat2−‐SQ⋅− and SQ⋅−‐SQ⋅− states (cat2−=catecholate, SQ⋅−=semiquinonate): [{CoIII(tpa)}2(theacat−cat)](PF6)2 (1), [{CoIII(tpa)}2(theacat−SQ)](PF6)3 (2), and [{CoIII(tpa)}2(theaSQ−SQ)](PF6)4 (3) (theaH4=2,3,6,7‐tetrahydroxy‐9,10‐dimethyl‐9,10‐dihydro‐9,10‐ethanoanthracene, tpa=tris(2‐pyridylmethyl)amine). Multi‐technique analysis confirms that 1, 2 and 3 adopt low spin‐CoIII containing {CoIII‐cat‐cat‐CoIII}, {CoIII‐cat‐SQ‐CoIII} and {CoIII‐SQ‐SQ‐CoIII} states, respectively. Compound 1 undergoes thermally‐induced {CoIII‐cat‐cat‐CoIII} ⇌ {CoIII‐cat‐SQ‐CoII} VT in the solid‐ and solution‐states above 300 K, involving high spin‐CoII. This interconversion is contrary to expectations, as tpa typically stabilizes low spin‐CoIII‐catecholate. Compound 2 is mixed‐valence class II/II–III, indicating a localized electronic structure, with electron transfer faster than the EPR and solvent rearrangement timescales, but slower than the infrared timescale. Compound 3 exhibits strong antiferromagnetic exchange. The overlap between the dioxolene π‐orbitals in thean− increases the accessibility of the cat2−‐SQ⋅− state, resulting in VT for 1. This study demonstrates that thean− can be isolated in multiple oxidation states in metal complexes, which is promising for applications in magnetic materials.

Molecular dynamics simulation of salt diffusion in constituting phosphazene-based polymer electrolyte

A growing demand to visualize polymer models in liquid poses a computational challenge in molecular dynamics (MD) simulation, as this requires emerging models under suitable force fields (FFs) to capture the underlying molecular behaviour accurately. In our present study, we have employed TIP3P potential on water and all atomistic optimized potentials for liquid simulations FFs to study the liquid electrolyte behavior of phosphazene-based polymer by considering its potential use in lithium-ion polymer batteries. We have explored the polymer's local structure, chain packing, wettability, and hydrophobic tendencies against the silicon surface using a combination of a pseudocontinuum model in MD simulation, and surface-sensitive sum frequency generation (SFG) vibrational spectroscopy. The finding yields invaluable insights into the molecular architecture of phosphazene. This approach identifies the importance of hydrophobic interactions with air and hydrophilic units with water molecules in understanding the behavior and properties of phosphazene-based polymers at interfaces, contributing to its advancements in materials science. The MD study uniquely captures traces of the polymer-ion linkage, which is observed to become more pronounced with the increase in polymer weight fraction. The theoretical observation of this linkage's influence on lithium-ion diffusion motion offers valuable insights into the fundamental physics governing the behavior of atoms and molecules within phosphazene-based polymer electrolytes in aqueous environments. Further these predictions are corroborated in the molecular-level depiction at the air-aqueous interface, as evidenced from the OH-oscillator strength variation measured by the SFG spectroscopy.The fundamental findings from this study open new avenues for utilizing MD simulation as a versatile methodology to gain profound insights into intermolecular interactions of polymer. It could be useful in the application of biomedical and energy-related research, such as polymer lithium-ion batteries, fuel cells, and organic solar cells.

Excited‐State Dynamics and Optical Properties of Silica Under Ultrafast Laser Irradiation

AbstractExcited by intense infrared ultrafast light pulses, a wide bandgap material undergoes nonlinear ionization, generating a high density of free electrons in conduction states. As a result, the electronic band structure is critically modified and the bandgap shrinks. This induces rapid changes in optical properties, dramatically affecting the absorption spectrum during light coupling to the dielectric surface or during nonlinear propagation inside the bulk. This study analyzes the structural behavior and the modification of the optical properties of laser‐excited silica glass at the molecular cluster level through first‐principles simulations. Employing density functional theory and the GW approximations for band structure under nonequilibrium conditions, alongside the Bethe–Salpeter equation, the dynamics of the optical properties of fused silica are comprehensively explored. The behavior of excited fused silica in a wide photon energy range (from a few to 20 eV) is thus predicted. Laser‐induced electron excitation triggers a redistribution of charges between oxygen and silicon atoms, accompanied by a significant increase in electronic pressure, local atomic structure rearrangement, and material expansion. Molecular dynamics simulations offer a temporal perspective on the excited state dynamics, unveiling the intricate interplay between electronic and atomic effects on bandgap evolution. The analysis sheds light on excitonic resonances, intraband and interband transitions in fused silica under ultrafast laser irradiation, providing valuable insights into its excited state behavior and optical properties.

Learning to Allocate Time-Bound and Dynamic Tasks to Multiple Robots Using Covariant Attention Neural Networks

Abstract In various applications of multi-robotics in disaster response, warehouse management, and manufacturing, tasks that are known a priori and tasks added during run time need to be assigned efficiently and without conflicts to robots in the team. This multi-robot task allocation (MRTA) process presents itself as a combinatorial optimization (CO) problem that is usually challenging to be solved in meaningful timescales using typical (mixed)integer (non)linear programming tools. Building on a growing body of work in using graph reinforcement learning to learn search heuristics for such complex CO problems, this paper presents a new graph neural network architecture called the covariant attention mechanism (CAM). CAM can not only generalize but also scale to larger problems than that encountered in training, and handle dynamic tasks. This architecture combines the concept of covariant compositional networks used here to embed the local structures in task graphs, with a context module that encodes the robots’ states. The encoded information is passed onto a decoder designed using multi-head attention mechanism. When applied to a class of MRTA problems with time deadlines, robot ferry range constraints, and multi-trip settings, CAM surpasses a state-of-the-art graph learning approach based on the attention mechanism, as well as a feasible random-walk baseline across various generalizability and scalability tests. Performance of CAM is also found to be at par with a high-performing non-learning baseline called BiG-MRTA, while noting up to a 70-fold improvement in decision-making efficiency over this baseline.

Multichannel Adaptive Data Mixture Augmentation for Graph Neural Networks

Graph neural networks (GNNs) have demonstrated significant potential in analyzing complex graph-structured data. However, conventional GNNs encounter challenges in effectively incorporating global and local features. Therefore, this paper introduces a novel approach for GNN called multichannel adaptive data mixture augmentation (MAME-GNN). It enhances a GNN by adopting a multi-channel architecture and interactive learning to effectively capture and coordinate the interrelationships between local and global graph structures. Additionally, this paper introduces the polynomial–Gaussian mixture graph interpolation method to address the problem of single and sparse graph data, which generates diverse and nonlinear transformed samples, improving the model's generalization ability. The proposed MAME-GNN is validated through extensive experiments on publicly available datasets, showcasing its effectiveness. Compared to existing GNN models, the MAME-GNN exhibits superior performance, significantly enhancing the model's robustness and generalization ability.

The structure and catalytic mechanism of a pseudoknot-containing hammerhead ribozyme

We have determined the crystal structure of a pseudoknot (PK)-containing hammerhead ribozyme that closely resembles the pistol ribozyme, with essentially identical secondary structure and connectivity. The activity is more sensitive to deletion of the G8 2’OH than to the absence of magnesium ions, indicating that the catalytic mechanism is the same as the extended hammerhead, and distinct from the pistol ribozyme. Here we show that nucleophilic attack is almost perfectly in-line, and the G8 2’OH is well positioned to act as general acid, being directed towards the O5’ leaving group, and 2.9 Å away from it. Despite the similarity in overall structure to the pistol ribozyme, the local structure close to the cleavage site differs, and the PK hammerhead retains its unique mechanistic identity and demonstrates enhanced activity over other hammerhead ribozymes under standard conditions.

Correction to: Effect of Crystallization on Local Structure and Optical Properties of Glass with 2.5Li2O–50GeO2–37.5P2O5 Composition

Correction to: Effect of Crystallization on Local Structure and Optical Properties of Glass with 2.5Li2O–50GeO2–37.5P2O5 Composition

Local factors have a greater influence on the abundance of alfalfa weevil and its larval parasitoids than landscape complexity in heterogeneous landscapes

ContextThe alfalfa weevil Hypera postica Gyllenhal (Coleoptera: Curculionidae) is one of the most destructive pests of alfalfa worldwide. Both local and landscape-scale factors can significantly influence crop pests, natural enemies, and the effectiveness of biological control services, but the relative influence of these factors is unclear.ObjectivesWe investigated the influence of the local variables and surrounding landscape composition and configuration on the abundance of alfalfa weevil, and on the abundance and parasitism rates of its larval parasitoids, Bathyplectes spp.MethodsWe sampled 65 commercial alfalfa fields along the Ebro Basin, Spain, over a period of 3 years, recording the field characteristics and landscape structure at three buffer radii of 250, 500 and 1000 m from the center of each field.ResultsThe abundance of weevil larvae was positively associated with the field perimeter and with the uncut alfalfa surrounding the pipes of the sprinkler irrigation system, but only one configuration variable was positively correlated: the alfalfa edge density. No local characteristics or landscape structures were associated with the abundance of adult weevils. The abundance of Bathyplectes spp. adults was positively associated to local factors such as the densities of alfalfa weevils and aphids. Few landscape structure variables, such as alfalfa edge density and Simpson’s Diversity Index, had explanatory value only at 250 m buffer radius. The rate of larval parasitism was affected by local variables, such as alfalfa weevil abundance and field age.ConclusionOur results provide, for the first time in the Mediterranean region and Europe, evidence of the relative importance of landscape structure and local factors on the abundance of the alfalfa weevil and its larval parasitoids, Bathyplectes spp. The strongest influences were based on local characteristics.

Understanding the influence of local structure distortions on Na-ion migration in perovskite solid electrolytes

The perovskite structured oxides of composition ABO3 are considered strong candidates for solid-state electrolytes in all-solid-state batteries due to their chemical and structural flexibility. However, further improvements must be made before they become commercially viable, and this requires a clear understanding of the structure-property relationships. In this study, the local structure of the perovskite sodium-ion solid electrolyte series Na1/2−xLa1/2−xSr2xZrO3 (NLSZ, x = 14, 16, 18, 116) was investigated via neutron total scattering. Small-box modelling against the neutron pair distribution function with the orthorhombic Pbnm structure showed local-scale features that deviate from the average structure. Big-box modelling revealed significant differences between the bonding configurations of the different A-site cations, which impacts the ionic conductivity of the material. This study demonstrates how understanding local-scale disorder is important for tuning the structure-property relationships of inorganic solid-state electrolyte materials in sustainable battery technologies.

Consensus local graph for multiple kernel clustering

Due to the ability to represent the relationship among data, graph has received extensive attention in clustering field. As illustrated by existing studies, learning graph in kernel space is an effective way to capture the structure of data. Among kernel-based methods, multiple kernel learning (MKL) is increasingly popular since it can automatically utilize the complementary information contained in base kernels. Although many existing MKL-based graph learning algorithms have promising learning abilities, we observe that they (1) put too much attention on learning the consensus kernel which probably contains lots of redundant information and loses the diversity of base kernels; (2) ignore the local structure of the representations in multiple kernel spaces. To eliminate the bad effect of these issues, we propose a novel graph learning method, termed consensus local graph based on MKL (CLGMKL), for clustering. In CLGMKL, a low-rank kernel matrix is used to extract the discriminative information of each base kernel and a local graph is constructed to capture the local structure contained in each new kernel. Then CLGMKL combines these local graphs in a self-weighed way. Since the above processes are associated with each other, we jointly optimize them to obtain the overall optimality. An effective learning scheme with proved convergence is developed to optimize the combined objective function. Finally, extensive experiments on some popular datasets are conducted to test the effectiveness of the presented method. As illustrated, CLGMKL is more competitive than the state-of-the-art algorithms.

Bolt looseness localization with connection-stiffness-varying flange

Electromechanical impedance analysis is a traditional method to identify the occurrence of bolt looseness, but accurate localization of blot looseness is hard to realize on the flange. In this study, a flange model with bolt connection stiffness varying with position is proposed. The location of bolt looseness can be then determined from the impedance spectrum of the model since the uniformity and symmetry of the flange are broken. The analytic model is established to reveal the distinguishability of the eigenfrequency shifting characteristics when the connection stiffness at different positions changes. The frequency shifting sequence is extracted from the coupling impedance spectrum as a feature, and the correlation between the sequences corresponding to bolt looseness at different positions is low. The relationship between the sequence and the degree of looseness is highly related so that the unknown degree of looseness can be matched with the calibrated sequence to realize the localization of the looseness. Based on the distinguishability of the frequency shifting sequence, the connection-stiffness-varying model shows great potential in the field of flanges or other connecting structures for structural health monitoring and damage localization.

The Impact Armed Conflict on Economic Growth and Sustainability in South Sudan

Purpose: The study aimed to explore the multifaceted impact of armed conflict on economic growth and sustainability in South Sudan. It sought to understand how the conflict has hindered the country's development and prosperity by examining the complex relationship between armed conflict and economic decline. Methodology: A qualitative approach was utilized, employing thematic analysis to gather and analyze data from various sources, including reports by international organizations such as the World Bank, IMF, and UN, as well as government publications and peer-reviewed journals. This method identified key themes and trends related to the economic impact of the conflict. Findings: The study revealed that the prolonged armed conflict in South Sudan has severely compromised economic stability. Key findings include the destruction of infrastructure, such as roads, bridges, schools, and hospitals, which impeded economic activities, disrupted supply chains, and increased transportation costs, with rebuilding costs exceeding USD 1 billion. The conflict resulted in significant loss of life, injuries, and displacement, reducing the available workforce and diminishing productivity, leading to an estimated annual economic loss of USD 4 billion. The displacement of over 3.8 million people disrupted local economies, agricultural production, and social structures, causing economic losses of USD 2.5 billion annually due to reduced agricultural output. The conflict also led to a decline in GDP, high unemployment rates, and unsustainable economic practices, with the economy's dependence on oil exports increasing vulnerability to price fluctuations and disruptions. Unique Contribution to Theory, Practice and Policy: The study recommends prioritizing the restoration and rebuilding of critical infrastructure to revive economic activities and supply chains. It also suggests the implementation of policies to support displaced populations, enhance agricultural productivity, and diversify the economy away from oil dependency to reduce vulnerability. The study contributes to theory by providing a deeper understanding of the relationship between conflict and economic decline, and it offers practical insights for policymakers and international organizations on the necessary interventions to foster economic recovery and sustainable development in post-conflict settings. Additionally, the findings underscore the importance of comprehensive peacebuilding strategies that address both immediate humanitarian needs and long-term economic stabilization and growth.

Bilinear Distance Feature Network for Semantic Segmentation in PowerLine Corridor Point Clouds

Semantic segmentation of target objects in power transmission line corridor point cloud scenes is a crucial step in powerline tree barrier detection. The massive quantity, disordered distribution, and non-uniformity of point clouds in power transmission line corridor scenes pose significant challenges for feature extraction. Previous studies have often overlooked the core utilization of spatial information, limiting the network’s ability to understand complex geometric shapes. To overcome this limitation, this paper focuses on enhancing the deep expression of spatial geometric information in segmentation networks and proposes a method called BDF-Net to improve RandLA-Net. For each input 3D point cloud data, BDF-Net first encodes the relative coordinates and relative distance information into spatial geometric feature representations through the Spatial Information Encoding block to capture the local spatial structure of the point cloud data. Subsequently, the Bilinear Pooling block effectively combines the feature information of the point cloud with the spatial geometric representation by leveraging its bilinear interaction capability thus learning more discriminative local feature descriptors. The Global Feature Extraction block captures the global structure information in the point cloud data by using the ratio between the point position and the relative position, so as to enhance the semantic understanding ability of the network. In order to verify the performance of BDF-Net, this paper constructs a dataset, PPCD, for the point cloud scenario of transmission line corridors and conducts detailed experiments on it. The experimental results show that BDF-Net achieves significant performance improvements in various evaluation metrics, specifically achieving an OA of 97.16%, a mIoU of 77.48%, and a mAcc of 87.6%, which are 3.03%, 16.23%, and 18.44% higher than RandLA-Net, respectively. Moreover, comparisons with other state-of-the-art methods also verify the superiority of BDF-Net in point cloud semantic segmentation tasks.

Quantifying city freight mobility segregation associated with truck multi-tours behavior

Freight mobility segregation, a phenomenon like residential social segregation, is a crucial aspect of city landscapes that influences the livability and sustainability of cities. However, there is a deficiency in understanding the intrinsic complexity of freight mobility segregation, particularly regarding the micro-level truck behaviors. In this study, we develop a new approach to assess the degree of freight mobility segregation within cities by leveraging large-scale truck GPS data in Chinese cities. The analysis indicates the existence of freight mobility segregation in cities, where certain groups of trucks serve high-demand areas, while another group of trucks serves low-demand areas. The activity spaces of distinct truck groups are largely non-overlapping or segregated. To uncover the correlations between mobility segregation and truck operational patterns, we introduce two metrics to characterize truck multi-tours behavior, focusing on tour pattern predictability and activity explorability. By employing freight point-of-interest (POI) data, we further reveal the influence of local economic structures and industrial compositions on mobility segregation in cities. These findings enrich our understanding of the dynamics of city freight systems, offering implications for improving logistics efficiency and fostering sustainable city development.

Real-time tracking of structural evolution in 2D MXenes using theory-enhanced machine learning

In situ Electron Energy Loss Spectroscopy (EELS) combined with Transmission Electron Microscopy (TEM) has traditionally been pivotal for understanding how material processing choices affect local structure and composition. However, the ability to monitor and respond to ultrafast transient changes, now achievable with EELS and TEM, necessitates innovative analytical frameworks. Here, we introduce a machine learning (ML) framework tailored for the real-time assessment and characterization of in operando EELS Spectrum Images (EELS-SI). We focus on 2D MXenes as the sample material system, specifically targeting the understanding and control of their atomic-scale structural transformations that critically influence their electronic and optical properties. This approach requires fewer labeled training data points than typical deep learning classification methods. By integrating computationally generated structures of MXenes and experimental datasets into a unified latent space using Variational Autoencoders (VAE) in a unique training method, our framework accurately predicts structural evolutions at latencies pertinent to closed-loop processing within the TEM. This study presents a critical advancement in enabling automated, on-the-fly synthesis and characterization, significantly enhancing capabilities for materials discovery and the precision engineering of functional materials at the atomic scale.