纸托盘嵌入式底脚横向强度研究

根据纸质托盘结构及纸质运输包装材料的特点,提出了一种嵌入式底脚的纸质托盘结构。首先分析了嵌入式底脚与普通粘合式底脚的承载机理,研究发现,嵌入式底脚结构改善了普通粘合式底脚结构的承载方式。在横向载荷作用下,普通粘合式底脚承受剪切力作用只有一个承载面,总承载面积较小;嵌入式底脚承受多个面的剪切力及拉压力作用,承载总面积较大。随后,分别设计了开槽式、纸护角式两种嵌入式结构,并制备试样,对比分析了两种嵌入式底脚托盘与普通粘合式底脚托盘联结强度。研究表明,普通粘合式底脚横向承载能力较小;嵌入式底脚结构提高了底脚与托盘底板的胶粘面积及强度,能够承受更大的载荷及冲击力,新型结构是一种更可靠的纸质底脚与底板结合方式。     

Ultra-dense planar metallic nanowire arrays with extremely large anisotropic optical and magnetic properties

A nanofabrication method for the production of ultra-dense planar metallic nanowire arrays scalable to wafer-size is presented. The method is based on an efficient template deposition process to grow diverse metallic nanowire arrays with extreme regularity in only two steps. First, III–V semiconductor substrates are irradiated by a low-energy ion beam at an elevated temperature, forming a highly ordered nanogroove pattern by a “reverse epitaxy” process due to self-assembly of surface vacancies. Second, diverse metallic nanowire arrays (Au, Fe, Ni, Co, FeAl alloy) are fabricated on these III–V templates by deposition at a glancing incidence angle. This method allows for the fabrication of metallic nanowire arrays with periodicities down to 45 nm scaled up to wafer-size fabrication. As typical noble and magnetic metals, the Au and Fe nanowire arrays produced here exhibited large anisotropic optical and magnetic properties, respectively. The excitation of localized surface plasmon resonances (LSPRs) of the Au nanowire arrays resulted in a high electric field enhancement, which was used to detect phthalocyanine (CoPc) in surface-enhanced Raman scattering (SERS). Furthermore, the Fe nanowire arrays showed a very high in-plane magnetic anisotropy of approximately 412 mT, which may be the largest in-plane magnetic anisotropy field yet reported that is solely induced via shape anisotropy within the plane of a thin film.

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Magnetic labeling of natural lipid encapsulations with iron-based nanoparticles

With superior biocompatibility and unique magnetic properties, iron-based nanoparticles (IBNP) are commonly encapsulated in cells and extracellular vesicles (EV) to allow for magnetic force controlled drug delivery and non-invasive tracking. Based on their natural source and similar morphology, we classify both cells and EVs as being natural lipid encapsulations (NLEs), distinguishing them from synthetic liposomes. Both their imaging contrast and drug effects are dominated by the amount of iron encapsulated in each NLE, demonstrating the importance of magnetic labeling efficiency. It is known that the membranes function as barriers to ensure that substances pass in and out in an orderly manner. The most important issue in increasing the cellular uptake of IBNPs is the interaction between the NLE membrane and IBNPs, which has been found to be affected by properties of the IBNPs as well as NLE heterogeneity. Two aspects are important for effective magnetic labelling: First, how to effectively drive membrane wrapping of the nanoparticles into the NLEs, and second, how to balance biosafety and nanoparticle uptake. In this review, we will provide a systematic overview of the magnetic labeling of NLEs with IBNPs. This article provides a summary of the applications of magnetically labeled NLEs and the labeling methods used for IBNPs. The review also analyzes the role of IBNPs physicochemical properties, especially their magnetic properties, and the heterogeneity of NLEs in the internalization pathway. At the same time, the future development of magnetically labeled NLEs is also discussed.

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Synthesis of ultrathin semicircle-shaped copper nanowires in ethanol solution for low haze flexible transparent conductors

Nano Research - Copper nanowires (CuNWs) are becoming an indispensable item for next-generation transparent optical devices due to their excellent conductivity and transparency. In this work,...     

<Emphasis Type="Italic">In-situ</Emphasis> liquid-cell TEM study of radial flow-guided motion of octahedral Au nanoparticles and nanoparticle clusters

The dynamic behavior of octahedral gold nanoparticles (NPs) and nanoparticle clusters (NPCs) in aqueous solution is studied by in-situ liquid-cell transmission electron microscopy (TEM). The octahedral Au NPs/NPCs show preferential orientations in the liquid cell, due to the interaction with the SiN_x window. The Au NPs show long-range reversible hopping and three-dimensional (3D) rotational motions in the liquid environment. At the same time, the Au NPCs and NPs perform slow stick-slip and stick-roll motions, respectively, with a centripetal trend. The centripetal motions were explained by a liquid evaporation-induced radial flow model, in which the NPCs/NPs trajectories are controlled by Stokes forces and surface friction by the silicon nitride window. The calculated radius-dependent force (F_c) on the NPCs/NPs shows a semi-linear correlation with the distance r between the NPCs/NPs and the center of mass, accompanied with stochastic fluctuations, in agreement with the model predictions. This work thus demonstrates the effectiveness of in situ liquid-cell TEM for the in-depth understanding of complicated liquid flow and force interactions in nanomaterials.

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超声作用下刚性壁面附近的双气泡脉动

在实际应用时,空化泡可能位于刚性壁附近。对刚性壁附近的空化泡脉动进行研究有利于更好地利用声空化。文章研究了刚性壁附近双气泡的动力学规律。研究结果表明,当两气泡与刚性壁距离相同时,气泡与壁之间的距离越大,刚性壁对辐射声波的反射越小,气泡脉动时能够达到的最大半径与最小半径的比值(即压缩比)也越大。若改变单个气泡与刚性壁的距离,则当两个气泡距离接近时,位置固定的气泡压缩比会减小。增大单个气泡的平衡半径,会使得两气泡脉动时的压缩比变小。此外,文章还对两气泡间距固定情况下,气泡压缩比与两气泡中心连线和壁面所成夹角之间的关系进行了讨论。     

全组分木质活性炭球的制备及其乙烯吸附性能研究

目的 制备全组分木质活性炭球,应用于乙烯吸附,筛选出乙烯吸附效果最好的活性炭球制备工艺。方法 以木材液化物为原料,探究不同的固化时间对制备的全组分木质活性炭球微观形貌的影响,利用场发射扫描电子显微镜观察其微观结构,通过氮气吸附-解析等温线计算样品孔径分布及其比表面积;使用气相色谱仪,结合乙烯吸附标准曲线,分析时间及吸附剂用量对4种活性炭球吸附乙烯性能的影响。结果 随着固化时间的增加,碳球表面趋于光滑、内部结构越来越致密,石墨化程度逐渐提高,比表面积和孔容逐渐减小。当固化时间为0.5 h时,比表面积高达2 073 m²/g,乙烯吸附量高达197.99 mg/g,约3 h达到吸附平衡。结论 全组分木质活性炭球能有效吸附乙烯气体,固化时间太长,活性炭球内部孔隙减少,不利于吸附乙烯气体。活性炭球孔容越大,乙烯吸附效果越好。     

Computational Parametric Analysis of Cellular Solids with the Miura-Ori Metamaterial Geometry under Quasistatic Compressive Loads

Origami-based metamaterials have widespread application prospects in various industries including aerospace, automotive, flexible electronics, and civil engineering structures. Among the wide range of origami patterns, the fourfold tessellation known as Miura-ori is of particular attraction to engineers and designers. More specifically, researchers have proposed different 3D structures and metamaterials based on the geometric characteristics of this classic origami pattern. Herein, a computational modeling approach for the design and evaluation of 3D cellular solids with the Miura-ori metamaterial geometry which can be of zero or nonzero thicknesses is presented. To this end, first, a range of design alternatives generated based on a numerical parametric model is designed. Next, their mechanical properties and failure behavior under quasistatic axial compressive loads along three perpendicular directions are analyzed. Then, the effects of various geometric parameters on their energy absorption behavior under compression in the most appropriate direction are investigated. The findings of this study provide a basis for future experimental investigations and the potential application of such cellular solids for energy-absorbing purposes.     

A Framework of Deep Optimal Features Selection for Apple Leaf Diseases Recognition

Identifying fruit disease manually is time-consuming, expert-required, and expensive; thus, a computer-based automated system is widely required. Fruit diseases affect not only the quality but also the quantity. As a result, it is possible to detect the disease early on and cure the fruits using computer-based techniques. However, computer-based methods face several challenges, including low contrast, a lack of dataset for training a model, and inappropriate feature extraction for final classification. In this paper, we proposed an automated framework for detecting apple fruit leaf diseases using CNN and a hybrid optimization algorithm. Data augmentation is performed initially to balance the selected apple dataset. After that, two pre-trained deep models are fine-tuning and trained using transfer learning. Then, a fusion technique is proposed named Parallel Correlation Threshold (PCT). The fused feature vector is optimized in the next step using a hybrid optimization algorithm. The selected features are finally classified using machine learning algorithms. Four different experiments have been carried out on the augmented Plant Village dataset and yielded the best accuracy of 99.8%. The accuracy of the proposed framework is also compared to that of several neural nets, and it outperforms them all.     

WiMA: Towards a Multi-Criterion Association in Software Defined Wi-Fi Networks

Despite the planned installation and operations of the traditional IEEE 802.11 networks, they still experience degraded performance due to the number of inefficiencies. One of the main reasons is the received signal strength indicator (RSSI) association problem, in which the user remains connected to the access point (AP) unless the RSSI becomes too weak. In this paper, we propose a multi-criterion association (WiMA) scheme based on software defined networking (SDN) in Wi-Fi networks. An association solution based on multi-criterion such as AP load, RSSI, and channel occupancy is proposed to satisfy the quality of service (QoS). SDN having an overall view of the network takes the association and reassociation decisions making the handoffs smooth in throughput performance. To implement WiMA extensive simulations runs are carried out on Mininet-NS3-Wi-Fi network simulator. The performance evaluation shows that the WiMA significantly reduces the average number of retransmissions by 5%–30% and enhances the throughput by 20%–50%, hence maintaining user fairness and accommodating more wireless devices and traffic load in the network, when compared to traditional client-driven (CD) approach and state of the art Wi-Balance approach.