Mechanically Robust,Ultraelastic Hierarchical Foam with Tunable Properties via 3D Printing

A mechanically robust, ultraelastic foam with controlled multiscale architectures and tunable mechanical/conductive performance is fabricated via 3D printing. Hierarchical porosity, including both macro‐ and microscaled pores, are produced by the combination of direct ink writing (DIW), acid etching, and phase inversion. The thixotropic inks in DIW are formulated by a simple one‐pot process to disperse duo nanoparticles (nanoclay and silica nanoparticles) in a polyurethane suspension. The resulting lightweight foam exhibits tailorable mechanical strength, unprecedented elasticity (standing over 1000 compression cycles), and remarkable robustness (rapidly and fully recover after a load more than 20 000 times of its own weight). Surface coating of carbon nanotubes yields a conductive elastic foam that can be used as piezoresistivity sensor with high sensitivity. For the first time, this strategy achieves 3D printing of elastic foam with controlled multilevel 3D structures and mechanical/conductive properties. Moreover, the facile ink preparation method can be utilized to fabricate foams of various materials with desirable performance via 3D printing.     

机械结构分层优化技术研究进展

复杂机械结构优化设计一般表现为多目标、多约束、多参数的优化问题,所以复杂结构优化设计过程通常存在计算复杂、不易收敛等困难。分层优化技术是复杂结构优化设计的一种有效途径,通过将优化问题中复杂的约束、设计变量以及功能目标合理分解为若干子层问题进行求解,然后通过协调得到复杂机械结构整体优化问题的结果。文章对机械结构分层优化技术的理论、应用研究现状进行了总结并探讨了其关键技术环节及发展趋势。     

多级孔HZSM-5分子筛催化快速热解生物质制芳烃

采用酸和/或碱处理法制备了一系列多级孔HZSM-5分子筛，采用XRD、N₂吸附、XRF、TEM、²⁷Al MAS NMR和NH₃-TPD等表征手段对其孔道结构和酸性进行表征。表征结果表明，采用碱处理方法，可获得孔径集中于3~6 nm的介孔结构，通过改变酸、碱处理次序，可调变酸中心数量和强酸/总酸中心比例。在Py-GC/MS装置上，以纤维素和水稻秸秆为原料，研究多级孔分子筛结构对催化快速热解（CFP）制芳烃反应的影响。反应评价结果表明，同商品级HZSM-5相比，采用先碱后酸处理获得的多级孔HZSM-5分子筛（HZ-OH/H），可将纤维素CFP芳烃碳产率由32.3%提高至43.6%，可将水稻秸秆CFP芳烃碳产率由23.0%提高至30.8%。多级孔HZ-OH/H分子筛的孔道结构和酸中心分布特征，对开发应用于生物质制芳烃的高效工业催化剂具有借鉴意义。     

Progress in the design of zeolite catalysts for biomass conversion into biofuels and bio-based chemicals

This article reviews the recent advances in the development of zeolite catalysts for biomass valorization processes to produce both biofuels and/or bio-based chemicals, which is an emerging and fast expanding field. The work deals with different types of feedstocks, including vegetable oils, lignocellulose and sugars, as well as with a number of relevant intermediates and platform molecules. Transformation of biomass into valuable products is hindered by a number of factors, mainly related to its complex composition, as biomass typically consists of bulky molecules with high oxygen content. Accordingly, biomass processing usually requires the combination of multiple steps and severe conditions, hence concepts like atom efficiency, product selectivity, and catalyst deactivation become of special relevance. A great progress has been achieved in the past years engineering the properties of zeolites for being adapted to the challenges associated to biomass valorization. The possibility of tailoring the main physicochemical properties of zeolites has become now a reality, being the major reason that explains the success achieved by this class of materials in a growing variety of biomass conversion pathways, as those described in this work: catalytic cracking and pyrolysis, hydrotreatments, with special relevance for hydrodeoxygenation processes, as well as in a high number of condensation, isomerization, and dehydration reactions. Thus, the development of hierarchical zeolites, exhibiting enhanced accessibility, and the possibility of introducing and combining in a controlled way different types of active sites (Brønsted and Lewis acid centers, basic sites, and metal phases) are the main basis of the excellent performance of zeolites in numerous biomass conversion routes.     

Scalable Production of Wearable Solid-State Li-Ion Capacitors from N-Doped Hierarchical Carbon

Smart and wearable electronics have aroused substantial demand for flexible portable power sources, but it remains a large challenge to realize scalable production of wearable batteries/supercapacitors with high electrochemical performance and remarkable flexibility simultaneously. Here, a scalable approach is developed to prepare wearable solid-state lithium-ion capacitors (LICs) with superior performance enabled by synergetic engineering from materials to device architecture. Nitrogen-doped hierarchical carbon (HC) composed of 1D carbon nanofibers welded with 2D carbon nanosheets is synthesized via a unique self-propagating high-temperature synthesis (SHS) technique, which exhibits superior electrochemical performance. Subsequently, inspired by origami, here, wave-shaped LIC punch-cells based on the above materials are designed by employing a compatible and scalable post-imprint technology. Finite elemental analysis (FEA) confirms that the bending stress of the punch-cell can be offset effectively, benefiting from the wave architecture. The wearable solid-state LIC punch-cell exhibits large energy density, long cyclic stability, and superior flexibility. This study demonstrates great promise for scalable fabrication of wearable energy-storage systems.     

基于动态标签的关系抽取方法

针对远程监督数据集的关系抽取研究方法存在着大量标签噪声的问题,提出了一种作用于分层注意力机制关系抽取模型的动态标签方法。首先，提出了一种根据关系类别相似性生成动态标签的概念。由于相同的关系标签包含相似的特征信息，计算特征信息的关系类别相似性有助于生成与特征信息相对应的动态标签。其次，利用动态标签方法的评分函数来评价远程监督标签是否为噪声，以决定是否需要生成新的标签代替远程监督标签，通过调整远程监督标签来抑制标签噪声对模型的影响。最后，根据动态标签来更新分层注意力机制以关注有效实例，重新学习每个有效实例的重要性，进一步抽取关键的关系特征信息。实验结果表明，相较于原始的分层注意力机制关系抽取模型，所提方法在Micro和Macro分数上分别有1.3个百分点和1.9个百分点的提升，实现了噪声标签的动态纠正，提升了模型的关系抽取能力。     

Image-Based Lifelogging: User Emotion Perspective

Lifelog is a digital record of an individual’s daily life. It collects, records, and archives a large amount of unstructured data; therefore, techniques are required to organize and summarize those data for easy retrieval. Lifelogging has been utilized for diverse applications including healthcare, self-tracking, and entertainment, among others. With regard to the image-based lifelogging, even though most users prefer to present photos with facial expressions that allow us to infer their emotions, there have been few studies on lifelogging techniques that focus upon users’ emotions. In this paper, we develop a system that extracts users’ own photos from their smartphones and configures their lifelogs with a focus on their emotions. We design an emotion classifier based on convolutional neural networks (CNN) to predict the users’ emotions. To train the model, we create a new dataset by collecting facial images from the CelebFaces Attributes (CelebA) dataset and labeling their facial emotion expressions, and by integrating parts of the Radboud Faces Database (RaFD). Our dataset consists of 4,715 high-resolution images. We propose Representative Emotional Data Extraction Scheme (REDES) to select representative photos based on inferring users’ emotions from their facial expressions. In addition, we develop a system that allows users to easily configure diaries for a special day and summaize their lifelogs. Our experimental results show that our method is able to effectively incorporate emotions into lifelog, allowing an enriched experience.     

Three-stage least squares-based iterative estimation algorithms for bilinear state-space systems based on the bilinear state estimator

Because of the product item of the control input and the state vector, the identification of bilinear systems is difficult. This paper considers the combined parameter and state estimation problems of bilinear state-space systems. On the basis of the observability canonical form and the model transformation, an identification model with a linear combination of the system parameters is obtained. Using the hierarchical principle, the identification model is decomposed into three submodels with fewer variables, and a three-stage least squares-based iterative (3S-LSI) algorithm is presented to estimate the system parameters. Furthermore, we derive a state estimator (SE) for estimating the unknown states, and present an SE-3S-LSI algorithm for estimating the unknown parameters and states simultaneously. After that, the least squares-based iterative algorithm is presented as a comparison. By analyzing the estimation results and the calculation amount, these two algorithms can identify the bilinear system effectively but the 3S-LSI algorithm can improve the computational efficiency. The simulation results indicate the effectiveness of the proposed algorithms.