High Structural Quality Research Articles

Oxidation resistance of Mo/Cr bilayer coating on Zr alloy in a 1200 °C steam environment

The Cr-coated Zr alloys demonstrate excellent resistance to high-temperature steam oxidation. However, the rapid diffusion pathways for oxygen formed by the inter-diffusion between the coating and alloy at high temperatures significantly affect the steam oxidation resistance of the coated alloys. To address this issue, we developed a Mo/Cr bilayer coating on Zr alloy by a combination of dc-MS and HiPIMS surface treatments. The coating exhibits outstanding steam oxidation resistance at high temperatures, resulting in a mass gain approximately 86.6% and 44.1% lower than that of the bare Zr alloy and Cr coating, respectively, after 30 min of steam oxidation at 1200 °C. This is mainly because, during the oxidation process, the Mo interface layer undergoes a transformation into a thin and high-quality double diffusion layer structure, effectively avoiding high-temperature inter-diffusion between the Cr coating and Zr alloy, thereby inhibiting the formation of oxygen diffusion pathways.

Quasi-Hyperbolic Framework Graphite Foam-Based Composites with High Thermal Conductivity and Electromagnetic Shielding Properties Fabricated by an Electrochemical Expansion Method.

Nowadays, the rapid development of electronic devices requires composites with high thermal conductivity and good electromagnetic shielding properties. The key challenge lies in the construction of high-performance conductive networks. Herein, an electrochemical expansion graphite foam (EEG) with a quasi-hyperbolic framework was prepared by an electrochemical expansion method, and then the epoxy resin (EP) was filled to fabricate the composites. The graphite plate was first electrochemically intercalated and then foamed, in which plasticization was caused by weak oxidation in intercalation and the quasi-hyperbolic framework was induced by foaming during expansion. These processes were characterized by Fourier transform infrared (FTIR), micro-Raman, X-ray photoelectron spectroscopy (XPS), and so on. Based on the highly efficient quasi-hyperbolic framework and high-quality graphite structure, the thermal conductivity of the composite reached 43.523 W/(m·K), and total electromagnetic interference (EMI) shielding (SET) reached 105 dB. The heat transfer behavior was simulated by finite element analysis (FEA) in detail. This method of preparing high thermal conductivity and electromagnetic shielding materials has a good application prospect.

Direct-Write Printed Slippery Surface for Assembling a High-Quality Graphene Structure and Its Application in Flexible Electric Actuators.

Graphene is a two-dimensional honeycomb-like nanomaterial generated by carbon atoms in sp2 hybridized orbitals to form a hexagonal lattice structure with excellent electrical, optical, and mechanical properties. The solution process method has been widely used to realize large-area patterned graphene structures for high-performance devices. In the method, graphene usually needs to be dispersed in solution, and the π-π bonding gravitational interactions between graphene sheets would lead to uncontrollable structures in solution and difficulty in obtaining high performance. In this work, a patterned graphene oxide (GO) structure with controllable thickness and layer spacing was realized by a direct-write printed slippery surface, which was used as a slippery limited template. After reducing GO into reduced graphene oxide (rGO), a flexible electric pattern with a conductivity of up to 6.425 × 103 S/m was realized. Furthermore, the patterned rGO structure was transferred on polydimethylsiloxane (PDMS), which could generate less than a 5% change in resistance after 10,000 consecutive bends, and an anisotropic expansion based on rGO and PDMS materials under electro-thermal coupling. The patterned rGO structures could meet the performance requirements of highly sensitive and complex deformation applications as flexible electric actuators. This study provides great research significance and application value for patterning high-quality graphene structures and realizing high-performance flexible electronic devices.

Crystallization and In Situ Room Temperature Data Collection Using the Crystallization Facility at Harwell and Beamline VMXi, Diamond Light Source.

Protocols for robotic protein crystallization using the Crystallization Facility at Harwell and in situ room temperature data collection from crystallization plates at Diamond Light Source beamline VMXi are described. This approach enables high-quality room-temperature crystal structures to be determined from multiple crystals in a straightforward manner and provides very rapid feedback on the results of crystallization trials as well as enabling serial crystallography. The value of room temperature structures in understanding protein structure, ligand binding, and dynamics is becoming increasingly recognized in the structural biology community. This pipeline is accessible to users from all over the world with several available modes of access. Crystallization experiments that are set up can be imaged and viewed remotely with crystals identified automatically using a machine learning tool. Data are measured in a queue-based system with up to 60° rotation datasets from user-selected crystals in a plate. Data from all the crystals within a particular well or sample group are automatically merged using xia2.multiplex with the outputs straightforwardly accessed via a web browser interface.

Rapid generation of high-quality structure figures for publication with PyMOL-PUB.

The advancement of structural biology has increased the requirements for researchers to quickly and efficiently visualize molecular structures in silico. Meanwhile, it is also time-consuming for structural biologists to create publication-standard figures, as no useful tools can directly generate figures from structure data. Although manual editing can ensure that figures meet the standards required for publication, it requires a deep understanding of software operations and/or program call commands. Therefore, providing interfaces based on established software instead of manual editing becomes a significant necessity. We developed PyMOL-PUB, based on the original design of PyMOL, to effectively create publication-quality figures from molecular structure data. It provides functions including structural alignment methods, functional coloring schemes, conformation adjustments, and layout plotting strategies. These functions allow users to easily generate high-quality figures, demonstrate structural differences, illustrate inter-molecular interactions, and predict performances of biomacromolecules. Our tool is publicly available at https://github.com/BGI-SynBio/PyMOL-PUB.

Modelling and experimental investigation of micro-dimpled structures milling with spiral trajectory tool reciprocating motion

To mill fine and well-defined micro-dimpled structures, a machining manner of spiral trajectory tool reciprocating motion, where the tool repeats the process of ‘feed milling–retract–cutting feed–feed milling again’ along the spiral trajectory, was proposed. From the kinematics analysis, it is found that the machining quality of micro-dimpled structures is highly dependent on the machining trajectory using spiral trajectory tool reciprocating motion. To reveal this causation, simulation modelling and experimental studies were carried out. A simulation model was developed to quantitatively and qualitatively investigate the influence of the trajectory discretization strategies (constant-angle and constant-arc length) and parameters (discrete angle, discrete arc length, and pitch) on surface texture and residual height of micro-dimpled structures. Subsequently, micro-dimpled structures were milled under different trajectory discretization strategies and parameters with spiral trajectory tool reciprocating motion. A comprehensive comparison between the milled results and simulation analysis was made based on geometry accuracy, surface morphology and surface roughness of milled dimples. Meanwhile, the errors and factors affecting the above three aspects were analyzed. The results demonstrate both the feasibility of the established simulation model and the machining capability of this machining way in milling high-quality micro-dimpled structures. Spiral trajectory tool reciprocating motion provides a new machining way for milling micro-dimpled structures and micro-dimpled functional surfaces. And an appropriate machining trajectory can be generated based on the optimized trajectory parameters, thus contributing to the improvement of machining quality and efficiency.

A study on the formation and failure mechanisms of CF/PPS-metal induction welding joints strengthened by micro-pins

This study proposed a novel hybrid joining technique that combines through-thickness reinforcement (TTR) and induction welding methods to address the challenges of composite-metal joining. The effects of geometrical parameters of micro-pins on the formation and bearing performance of hybrid joints were investigated by combing the experimental and numerical simulation approaches. Two simulation models which included the induction heating transfer and joint tensile failure process were established by COMSOL Multiphysics and Abaqus/Explicit. Subsequently, digital image correlation (DIC) was used to monitor the deformation process of different types of joints under tensile load, and a scanning electron microscope (SEM) was used to observe the welding interface of failed joints. By comparing the experimental and simulation results, it is found that adding pins can significantly improve the mechanical performance of welded joints, with maximum increases of 159% and 1758% in ultimate strength and energy absorption respectively compared to welded joints without interlock structures. This technique presents a potential solution for achieving high-quality metal-composite welded structures.

Role of carbon in n-type bulk GaN crystals

In this work co-doping with silicon and carbon in gallium nitride grown by halide vapor phase epitaxy was performed. Native seeds of high structural quality with bowing radii of (0001) crystallographic planes higher than 15 m and threading dislocation density on the order of 5 × 104 cm−3 were used. The crystallized material was examined in terms of structural, optical, and electrical properties. For this purpose different characterization methods: X-ray diffraction, Raman spectroscopy, low-temperature photoluminescence, and Hall measurements, were applied. The physical properties of samples doped only with silicon and co-doped with carbon were analyzed and compared. However, during the crystallization process, due to high amount of Si and C, clusters of silicon carbide were unintentionally formed. The formation of these SiC clusters had an impact on the properties of the GaN crystals that were being studied. The research provides an initial insight into the complex studies of co-doping gallium nitride crystals. The results of this paper give us groundwork for more in-depth exploration of this topic.

Self-restrained contrastive enhanced network for graph structure learning

Existing graph neural networks (GNNs) are mostly applied to representation scenes with complete graph structure. However, the graph structures of complex systems from the real world are generally missing or misconnected. In addition, due to the indistinguishability of graph node features, most of the existing GNNs are limited by the over-smoothing problem when performing classification tasks. To address these issues, we propose a model for learning and optimizing graph structure—Self-restrained Graph Contrastive Enhanced Network (GCEN). Firstly, the multiple branch generation structure is adopted to obtain various graph structures, which are updated by multi-order information fusion to obtain a new graph structure. Secondly, the performance of each generated structure branch is improved by self-restrained network, and then the structure information of the graph is continuously optimized to obtain a high-quality graph structure. Finally, the feature contrast enhancement module is utilized to adaptively adjust the features of nodes to obtain distinguishable features, thus alleviating the over-smoothing problem of GNNs in classification tasks. Extensive experiments on several benchmark datasets indicate that our GCEN is superior to several models for learning task-specific graph structure in optimizing the graph structure.

AlphaFold2 Reveals Structural Patterns of Seasonal Haplotype Diversification in SARS-CoV-2 Spike Protein Variants.

The slow experimental acquisition of high-quality atomic structures of the rapidly changing proteins of the COVID-19 virus challenges vaccine and therapeutic drug development efforts. Fortunately, deep learning tools such as AlphaFold2 can quickly generate reliable models of atomic structure at experimental resolution. Current modeling studies have focused solely on definitions of mutant constellations of Variants of Concern (VOCs), leaving out the impact of haplotypes on protein structure. Here, we conduct a thorough comparative structural analysis of S-proteins belonging to major VOCs and corresponding latitude-delimited haplotypes that affect viral seasonal behavior. Our approach identified molecular regions of importance as well as patterns of structural recruitment. The S1 subunit hosted the majority of structural changes, especially those involving the N-terminal domain (NTD) and the receptor-binding domain (RBD). In particular, structural changes in the NTD were much greater than just translations in three-dimensional space, altering the sub-structures to greater extents. We also revealed a notable pattern of structural recruitment with the early VOCs Alpha and Delta behaving antagonistically by suppressing regions of structural change introduced by their corresponding haplotypes, and the current VOC Omicron behaving synergistically by amplifying or collecting structural change. Remarkably, haplotypes altering the galectin-like structure of the NTD were major contributors to seasonal behavior, supporting its putative environmental-sensing role. Our results provide an extensive view of the evolutionary landscape of the S-protein across the COVID-19 pandemic. This view will help predict important regions of structural change in future variants and haplotypes for more efficient vaccine and drug development.

Influence of Process Energy on the Formation of Imperfections in Body-Centered Cubic Cells with Struts in the Vertical Orientation Produced by Laser Powder Bed Fusion from the Magnesium Alloy WE43.

The low specific density and good strength-to-weight ratio make magnesium alloys a promising material for lightweight applications. The combination of the properties of magnesium alloys and Additive Manufacturing by the Laser Powder Bed Fusion (LPBF) process enables the production of complex geometries such as lattice or bionic structures. Magnesium structures are intended to drastically reduce the weight of components and enable a reduction in fuel consumption, particularly in the aerospace and automotive industries. However, the LPBF processing of magnesium structures is a challenge. In order to produce high-quality structures, the process parameters must be developed in such a way that imperfections such as porosity, high surface roughness and dimensional inaccuracy are suppressed. In this study, the contour scanning strategy is used to produce vertical and inclined struts with diameters ranging from 0.5 to 3 mm. The combination of process parameters such as laser power, laser speed and overlap depend on the inclination and diameter of the strut. The process parameters with an area energy of 1.15-1.46 J/mm2 for struts with a diameter of 0.5 mm and an area energy of 1.62-3.69 J/mm2 for diameters of 1, 2 and 3 mm achieve a relative material density of 99.2 to 99.6%, measured on the metallographic sections. The results are verified by CT analyses of BCCZ cells, which achieve a relative material density of over 99.3%. The influence of the process parameters on the quality of struts is described and discussed.

Site-controlled preparation of metallic microstructures via mechanical scratch-induced selective electrodeposition

Controlled preparation of metallic microstructures attracts wide interest in sensor and electronic fields. However, limited by fabrication technology, it faces challenges in achieving template-free and orientational formation of metallic microstructures. Herein, a site-controlled strategy via scratch-induced selective electrodeposition was proposed to prepare high-quality metallic structures on silicon (Si) surface with the assistance of scanning probe lithography (SPL). The effects of scratching and depositing conditions were systematically investigated for optimizing the preparation process. Selective electrodeposition mechanisms were addressed through topographic and conductive detections. It was found that single-cycle scratch created under higher loads was beneficial for preparing compact and continuous Cu structures. Further analysis indicated that higher normal loads in scratching can facilitate obtaining more conductive sites, promoting orientated migration of metal ions during the reaction, while the increase of reciprocating cycles can cause more amorphous layers and hinder electron transport. Moreover, post-annealing treatment can lead to the growth of Cu grain sizes, which contributes to the crystallinity. Notably, the proposed method is demonstrated with the preparation of Cu coils, which is sensitive to alternating magnetic field. These findings shed new light on the site-controlled preparation of high-quality metallic microstructures and the applications.

Effects of spans on whipping capabilities of aerated emulsions: Reinforcement of fat crystal-membrane interactions

Poor whipping capabilities of anhydrous milkfat based aerated emulsions pose a fresh challenge to food industrial production, limiting their potential applications in tea drinks, coffee, cakes, and desserts. In this study, the effects of high melting- and low melting-Spans on whipping compatibilities of the aerated emulsions were investigated from view of fat crystal-membrane interactions. With an increase in Spans concentration, there was a corresponding decrease in both fat globule size and interfacial protein. However, in comparison with low melting-Span 20 and Span 80, Span 40 and Span 60 with higher melting points have been found to improve anhydrous milkfat crystal matrix in terms of dense (Df > 1.8) and spot-like crystals (5–10 μm). The interfacial film strength was reduced in this case as both long chain-saturated Spans competitively absorb at the membrane, and elasticity-dominant interfacial membrane was further obtained as their crystallization effects. Occurrence and development of partial coalescence of fat globules (>80% at more than 0.5% addition) were therefore promoted with such fat crystal-membrane interactions during whipping, contributing to a significant improvement in whipping capabilities and heat-stable stabilities of foam structures. A mechanism on the effects of Spans on improving whipping capabilities of aerated emulsions was therefore proposed based on the fat crystal-membrane interactions, which will provide guidance to produce high-quality whipped structures for aerated emulsions.

Community detection in decentralized social networks with local differential privacy

Community detection under local differential privacy has been a research hotspot recently. Most existing methods primarily rely on the edge local differential privacy model (edge-LDP), compromising privacy strength for high utility. In addition, they adopt a way of agglomerating nodes one by one to construct communities (or clusters) through multiple iterations, suffering from significant noise and low detection efficiency. Moreover, the error accumulation caused by large-scale iterations is not conducive to detection accuracy. To address these issues, we propose a privacy-preserving community detection method, CD-LDP, based on node-LDP, achieving efficient and accurate community construction by aggregating grouped units instead of individual nodes and delimiting the interaction to three rounds. Specifically, we propose a privacy-preserving global social network construction method to gain connectivity information among all nodes through two rounds of interactions. In addition, a novel perturbation mechanism based on the star social network structure is devised, which improves the accuracy of the global social network by reducing the probability of generating fake edges. Secondly, we design a privacy-preserving node grouping approach leveraging clustering coefficients and Haar discrete wavelet transform in the third round, achieving low noise injection and high-quality node grouping through lossless dimensionality reduction. Lastly, we develop a fast unit merging algorithm combining modularity and Huffman trees to realize efficient and accurate community building by reducing the aggregation possibility of nodes with low similarity. Theoretical analysis and experimentation on real-world datasets testify that our solution can efficiently extract high-quality community structures while satisfying node-LDP.

Application of Pebbles for Geotechnical Seismic Isolation (GSI): Experimental Parametric Study

Low-income regions do not have the economic strength to use conventional isolators; therefore, low-cost alternatives are needed. The recent series of earthquakes in Turkey has once again demonstrated the destructive power of this natural disaster and highlighted the need for high-quality earthquake-resistant structures. In this context, a comprehensive experimental parametric study on the use of natural stone pebbles (ASL-1) and stone pebbles in combination with a geogrid layer (ASL-2) as suitable materials for a GSI system is conducted and the main results are presented. The seismic behavior of five different models was tested using four different acceleration diagrams with different peak ground accelerations (PGAs). Characteristic displacements, accelerations, and strains were measured. The results and conclusions presented are based on the integration and synthesis of several previously conducted studies.

Exploring urban building space provision and inequality: A three-dimensional perspective on Chinese cities toward sustainable development goals

Accelerated urbanization and population expansion present substantial pressures on the built environment, particularly in terms of inadequate space and pronounced inequality of building provision. A comprehensive understanding of building space provision and inequality of city remains constrained. This study utilizes high-quality building structure data and fine-scale gridded population product to investigate the building space provision and inequality across 67 Chinese cities from a three-dimensional (3-D) perspective. We integrate both dimensions of building space provision and inequality to facilitate a more nuanced understanding of the built environment. With an overall average building volume per capita (perBV: 517.67 m3 /person) and Gini measures (BV-Gini: 0.497), our study reveals that tier-1 cities suffer shortage of building space, while rapidly developing tier-2 cities are confronted with high degree of inequality. The results indicate that urban areas are characterized by a more limited building space provision (perBV: 1,074.52m3/person for rural, and 500.83 m3/person for urban) and higher inequality (BV-Gini: 0.494 for urban, and 0.427 for rural) compared to rural areas. This study provides valuable insights into comprehension of the built environment from 3-D building perspective toward achieving adequate and equitable building space, consistent with the Sustainable Development Goals (SDGs) and the principles of New Urban Agenda.

Mechanical properties and microstructure of waterborne polyurethane-modified cement composites as concrete repair mortar

With the increase in service life, the durability of a large number of concrete structures deteriorates year by year under the influence of the external environment, requiring urgent repair and reinforcement. Ordinary cement concrete materials have disadvantages such as high brittleness, low tensile strength, poor bonding ability, and insufficient durability, making it difficult to meet the repair needs of high-quality building structures. Therefore, self-synthesized anion WPU was used to study its effect on the mechanical properties of cement-based repair mortar. The research found that waterborne polyurethane (WPU)-modified cementitious composite repair materials exhibit better bonding strength with old concrete substrates compared to traditional cement-based materials. Additionally, the incorporation of WPU significantly reduces the brittleness of the repair material, ensuring enhanced efficiency in the repair process. However, according to the hydration heat test results, the addition of WPU delays the hydration process of the composite repair materials. In addition, the addition of WPU enhances the flexibility and bonding ability of the materials, with flexural strength and tensile strength at the age of 28 days reaching 13 MPa and 6 MPa, respectively. Through stress‒strain curve analysis, it was found that the maximum strain amplification of WPU cement mortar can reach 134 %. Combined with SEM testing, it was observed that cured WPU films were present in specimens aged 28 days with P/C ratios of 10 % and 15 %. WPU and cement hydration products permeate each other, forming a three-dimensional polymer network structure, improving the pore structure, and fully exerting the flexibility of WPU. This also validates the experimental results of increased ultimate tensile strain. WPU cement-based composite repair materials have higher bonding strength at a high P/C ratio (15 %), and WPU alone, used as an interface agent for pretreating the interface of old concrete, can improve the interface bonding strength, up to twice that of the group without the surface agent.

A High-Quality Data Set of Protein-Ligand Binding Interactions Via Comparative Complex Structure Modeling.

High-quality protein-ligand complex structures provide the basis for understanding the nature of noncovalent binding interactions at the atomic level and enable structure-based drug design. However, experimentally determined complex structures are scarce compared with the vast chemical space. In this study, we addressed this issue by constructing the BindingNet data set via comparative complex structure modeling, which contains 69,816 modeled high-quality protein-ligand complex structures with experimental binding affinity data. BindingNet provides valuable insights into investigating protein-ligand interactions, allowing visual inspection and interpretation of structural analogues' structure-activity relationships. It can also be used for evaluating machine-learning-based scoring functions. Our results indicate that machine learning models trained on BindingNet could reduce the bias caused by buried solvent-accessible surface area, as we previously found for models trained on the PDBbind data set. We also discussed strategies to improve BindingNet and its potential utilization for benchmarking the molecular docking methods and ligand binding free energy calculation approaches. The BindingNet complements PDBbind in constructing a sufficient and unbiased protein-ligand binding data set and is freely available at http://bindingnet.huanglab.org.cn.

Functional domain annotation by structural similarity.

Traditional automated in silico functional annotation uses tools like Pfam that rely on sequence similarities for domain annotation. However, structural conservation often exceeds sequence conservation, suggesting an untapped potential for improved annotation through structural similarity. This approach was previously overlooked before the AlphaFold2 introduction due to the need for more high-quality protein structures. Leveraging structural information especially holds significant promise to enhance accurate annotation in diverse proteins across phylogenetic distances. In our study, we evaluated the feasibility of annotating Pfam domains based on structural similarity. To this end, we created a database from segmented full-length protein structures at their domain boundaries, representing the structure of Pfam seeds. We used Trypanosoma brucei, a phylogenetically distant protozoan parasite as our model organism. Its structome was aligned with our database using Foldseek, the ultra-fast structural alignment tool, and the top non-overlapping hits were annotated as domains. Our method identified over 400 new domains in the T. brucei proteome, surpassing the benchmark set by sequence-based tools, Pfam and Pfam-N, with some predictions validated manually. We have also addressed limitations and suggested avenues for further enhancing structure-based domain annotation.

The Impact of Spinning Speed on n-TiO2/ZnO Bilayer Thin Film Fabricated through Sol–Gel Spin-Coating Method

The spinning speed parameter plays a crucial role in determining the properties of an n-TiO2/ZnO bilayer thin film fabricated using the sol–gel spin-coating technique, especially for solar cell applications. In this study, various spinning speeds were employed on an n-TiO2/ZnO bilayer thin film, and characterizations were conducted, such as morphological, structural, and optical properties. The findings revealed that the optimal conditions for the thin film were achieved at a spinning speed of 3000 rpm. Under this condition, a homogenous and compact surface morphology was observed, with an even distribution of ZnO grains. The successful fabrication of an n-TiO2/ZnO bilayer thin film was confirmed by the presence of characteristic peaks for both TiO2 and ZnO. Obviously, three dominant ZnO orientation peaks, which included (100), (002), and (101) were identified. The prevalence of the (002)-ZnO orientation plane indicated a high-quality structure with excellent crystallinity. In terms of optical properties, the achievement of high transmittance up to 75% resembles the high transparency of the thin film. The optical energy of the n-TiO2/ZnO bilayer thin film is estimated at 3.10 eV. In summary, the spinning speed parameter played a pivotal role in enhancing various properties of the thin film, making it a significant factor in its development for diverse applications.