Inflating hollow nanocrystals through a repeated Kirkendall cavitation process.

The Kirkendall effect has been recently used to produce hollow nanostructures by taking advantage of the different diffusion rates of species involved in the chemical transformations of nanoscale objects. Here we demonstrate a nanoscale Kirkendall cavitation process that can transform solid palladium nanocrystals into hollow palladium nanocrystals through insertion and extraction of phosphorus. The key to success in producing monometallic hollow nanocrystals is the effective extraction of phosphorus through an oxidation reaction, which promotes the outward diffusion of phosphorus from the compound nanocrystals of palladium phosphide and consequently the inward diffusion of vacancies and their coalescence into larger voids. We further demonstrate that this Kirkendall cavitation process can be repeated a number of times to gradually inflate the hollow metal nanocrystals, producing nanoshells of increased diameters and decreased thicknesses. The resulting thin palladium nanoshells exhibit enhanced catalytic activity and high durability toward formic acid oxidation

Platinum-nickel alloy excavated nano-multipods with hexagonal close-packed structure and superior activity towards hydrogen evolution reaction

铂镍合金在氢析出(HER)、氧还原(ORR)等重要能量转化反应中具有优异催化性质，受到了人们广泛的关注。近日，谢兆雄教授课题组通过简单的溶剂热方法，首次合成出六方晶系的铂镍合金枝状纳米晶，其中每个枝杈结构由六个{11-20}高能晶面裸露的超薄纳米片组装而成。与面心立方晶系铂镍合金相比，亚稳态的六方晶系铂镍合金在HER反应中表现出更加优异的性质。当电流密度为10 mA·cm-2时，其过电位仅有65 mV，同时质量电流密度高达3.03 mA·µgPt-1 (-70 m V vs. RHE)，是目前为止报道的HER催化剂中质量活性最高的，其突出的催化性能主要来源于晶相作用(同质异晶)及大的比表面积。该项工作为发展高催化性能的铂基合金纳米晶提供了新的研究思路。该研究是在谢兆雄教授和蒋亚琪副教授指导下，与傅钢教授共同合作完成。实验部分由博士生曹振明（第一作者）、陈巧丽、沈守宇、卢邦安，硕士生李慧齐以及博士后张嘉伟共同完成，理论计算部分由傅钢教授课题组完成。【Abstract】Crystal phase regulations may endow materials with enhanced or new functionalities. However, syntheses of noble metal-based allomorphic nanomaterials are extremely difficult, and only a few successful examples have been found. Herein, we report the discovery of hexagonal close-packed Pt–Ni alloy, despite the fact that Pt–Ni alloys are typically crystallized in face-centred cubic structures. The hexagonal close-packed Pt–Ni alloy nano-multipods are synthesized via a facile one-pot solvothermal route, where the branches of nano-multipods take the shape of excavated hexagonal prisms assembled by six nanosheets of 2.5nm thickness. The hexagonal close-packed Pt–Ni excavated nano-multipods exhibit superior catalytic property towards the hydrogen evolution reaction in alkaline electrolyte. The overpotential is only 65mV versus reversible hydrogen electrode at a current density of 10 mAcm-2 , and the mass current density reaches 3.03mA µgPt-1 at -70mV versus reversible hydrogen electrode, which outperforms currently reported catalysts to the best of our knowledge.This work was supported by the National Basic Research Program of China (Grant 2015CB932301), the National Natural Science Foundation of China (Grants 21333008, 21603178 and J1030415) and the Natural Science Foundation of Fujian Province of China (No. 2014J01058). 该研究工作得到科技部（批准号：2015CB932301）、国家自然科学基金委（批准号：21333008, 21603178 和 J1030415）和福建省自然科学基金委（No. 2014J01058）的大力资助与支持

Behaviour of steel-reinforced concrete columns under combined torsion based on ABAQUS FEA

A computational model for studying the mechanical performance of steel-concrete columns under combined torsion is established via ABAQUS. The model is validated by experimental results. Through numerical simulations, the influence of the axial load ratio, torsion-bending ratio, concrete strength, steel ratio, longitudinal reinforcement ratio, stirrup ratio, and shear-span ratio on the torsional behaviour of steel-concrete columns is comprehensively investigated. The initial torsion stiffness and ultimate torsion strength of the column increase with increasing concrete strength and decreasing shear-span ratio. The parameters in descending order of influence on the ultimate torsion strength are steel ratio, torsion-bending ratio, tirrup ratio, longitudinal reinforcement ratio, and axial load ratio. Furthermore, the seven parameters in descending order of influence on the ductility coefficient are the steel ratio, shear-span ratio, concrete strength, axial load ratio, stirrup ratio, torsion-bending ratio and longitudinal reinforcement ratio

Application of Xanthan Gum as a Pre-Treatment and Sharpness Evaluation for Inkjet Printing on Polyester

Inkjet printing on polyester fabric displays versatile environmental advantages. One of the significant benefits of inkjet printing is a dramatic enhancement of the printing quality. In this study, xanthan gum—a bio-based thickening agent accompanied by several salts—was adopted for the pretreatment of polyester fabric aiming at improving the sharpness and color depth of inkjet printed patterns. The influences of four metal salts (NaCl, KCl, CaCl2 and MgCl2) on inkjet printing performance were studied. More importantly, a quantitative method for evaluating the sharpness of an inkjet printed pattern was established according to the characteristics of anisotropy and isotropy of diffusion and adsorption of ink droplets on a fiber surface. Results showed that xanthan gum along with a low dosage of bivalent salts can significantly improve the color depth (K/S value) and sharpness of the printed polyester fabrics. It is feasible to evaluate the sharpness of inkjet printed polyester fabrics using a five-stage system, selecting the inkjet ellipse coefficient (T) and inkjet ellipse area (S), which can provide a quantitative and rapid evaluation method for defining inkjet printing

Infrared Small Target Detection via Non-Convex Rank Approximation Minimization Joint <i>l</i>_2,1 Norm

To improve the detection ability of infrared small targets in complex backgrounds, a novel method based on non-convex rank approximation minimization joint l2,1 norm (NRAM) was proposed. Due to the defects of the nuclear norm and l1 norm, the state-of-the-art infrared image-patch (IPI) model usually leaves background residuals in the target image. To fix this problem, a non-convex, tighter rank surrogate and weighted l1 norm are instead utilized, which can suppress the background better while preserving the target efficiently. Considering that many state-of-the-art methods are still unable to fully suppress sparse strong edges, the structured l2,1 norm was introduced to wipe out the strong residuals. Furthermore, with the help of exploiting the structured norm and tighter rank surrogate, the proposed model was more robust when facing various complex or blurry scenes. To solve this non-convex model, an efficient optimization algorithm based on alternating direction method of multipliers (ADMM) plus difference of convex (DC) programming was designed. Extensive experimental results illustrate that the proposed method not only shows superiority in background suppression and target enhancement, but also reduces the computational complexity compared with other baselines

TFCD-Net: Target and False Alarm Collaborative Detection Network for Infrared Imagery

Infrared small target detection (ISTD) plays a crucial role in both civilian and military applications. Detecting small targets against dense cluttered backgrounds remains a challenging task, requiring the collaboration of false alarm source elimination and target detection. Existing approaches mainly focus on modeling targets while often overlooking false alarm sources. To address this limitation, we propose a Target and False Alarm Collaborative Detection Network to leverage the information provided by false alarm sources and the background. Firstly, we introduce a False Alarm Source Estimation Block (FEB) that estimates potential interferences present in the background by extracting features at multiple scales and using gradual upsampling for feature fusion. Subsequently, we propose a framework that employs multiple FEBs to eliminate false alarm sources across different scales. Finally, a Target Segmentation Block (TSB) is introduced to accurately segment the targets and produce the final detection result. Experiments conducted on public datasets show that our model achieves the highest and second-highest scores for the IoU, Pd, and AUC and the lowest Fa among the DNN methods. These results demonstrate that our model accurately segments targets while effectively extracting false alarm sources, which can be used for further studies