Beijing University of Technology

About: Beijing University of Technology is a education organization based out in Beijing, Beijing, China. It is known for research contribution in the topics: Microstructure & Laser. The organization has 31929 authors who have published 31987 publications receiving 352112 citations. The organization is also known as: Běijīng Gōngyè Dàxué & Beijing Polytechnic University.

Stable Zr(IV)-Based Metal-Organic Frameworks with Predesigned Functionalized Ligands for Highly Selective Detection of Fe(III) Ions in Water.

Abstract: Metal–organic frameworks are a class of attractive materials for fluorescent sensing. Improvement of hydrolytic stability, sensitivity, and selectivity of function is the key to advance application of fluorescent MOFs in aqueous media. In this work, two stable MOFs, [Zr6O4(OH)8(H2O)4(L1)2] (BUT-14) and [Zr6O4(OH)8(H2O)4(L2)2] (BUT-15), were designed and synthesized for the detection of metal ions in water. Two new ligands utilized for construction of the MOFs, namely, 5′,5‴-bis(4-carboxyphenyl)-[1,1′:3′,1″:4″,1‴:3‴,1′′′′-quinquephenyl]-4,4′′′′-dicarboxylate (L1) and 4,4′,4″,4‴-(4,4′-(1,4-phenylene)bis(pyridine-6,4,2-triyl))tetrabenzoate (L2), are structurally similar with the only difference being that the latter is functionalized by pyridine N atoms. The two MOFs are isostructural with a sqc-a topological framework structure, and highly porous with the Brunauer–Emmett–Teller (BET) surface areas of 3595 and 3590 m2 g–1, respectively. Interestingly, they show intense fluorescence in water, which can be sol...

Towards Environment Independent Device Free Human Activity Recognition

Abstract: Driven by a wide range of real-world applications, significant efforts have recently been made to explore device-free human activity recognition techniques that utilize the information collected by various wireless infrastructures to infer human activities without the need for the monitored subject to carry a dedicated device. Existing device free human activity recognition approaches and systems, though yielding reasonably good performance in certain cases, are faced with a major challenge. The wireless signals arriving at the receiving devices usually carry substantial information that is specific to the environment where the activities are recorded and the human subject who conducts the activities. Due to this reason, an activity recognition model that is trained on a specific subject in a specific environment typically does not work well when being applied to predict another subject's activities that are recorded in a different environment. To address this challenge, in this paper, we propose EI, a deep-learning based device free activity recognition framework that can remove the environment and subject specific information contained in the activity data and extract environment/subject-independent features shared by the data collected on different subjects under different environments. We conduct extensive experiments on four different device free activity recognition testbeds: WiFi, ultrasound, 60 GHz mmWave, and visible light. The experimental results demonstrate the superior effectiveness and generalizability of the proposed EI framework.

Neck Formation and Self-Adjusting Mechanism of Neck Growth of Conducting Powders in Spark Plasma Sintering

Abstract: By using spherical Cu powders as the conducting sintering material, the microstructures of sintered powder particles at different stages in the process of spark plasma sintering (SPS) have been investigated. Theoretical analyses are proposed to quantify the effects of the pulsed direct current on the neck formation and the neck growth of conducting powders. It is found that there is a considerable inhomogeneous distribution of the temperature increase from the particle-contacting surface to the center of the particle when the pulsed current passes through. The temperature at the particle-contacting surface may reach the boiling point of the material, which results in neck formation at relatively low-sintering temperatures through a process of local melting and rapid solidification. The neck growth depends on the local distribution of the current intensity, which is determined by the competition between the neck cross-sectional area and the electrical resistivity increasing with the temperature. Accordingly, we propose that the coarsening of necks follows a "self-adjusting" mechanism, which is likely to be the essential reason for the homogeneous distributions of neck sizes and sizes of fine grains formed in the neck zones during the SPS process.

Stratification of Extracellular Polymeric Substances (EPS) for Aggregated Anammox Microorganisms.

Abstract: Sludge aggregation and biofilm formation are the most effective approaches to solve the washout of anammox microorganisms. In this study, the structure and composition of EPS (extracellular polymeric substances) were investigated to elucidate the factors for the anammox aggregation property. Anammox sludge taken from 18 lab-scale and pilot-scale reactors treating different types of wastewater was analyzed using EEM-PARAFAC (excitation–emission matrix and parallel factor analysis), FTIR (Fourier transform infrared spectroscopy) and real-time PCR combined with multivariate statistical analysis. The results showed that slime and TB-EPS (tightly bound EPS) were closely related with water quality and sludge morphology, and could be used as the indicators for anammox microbial survival ability and microbial aggregate morphology. Furthermore, slime secreted from anammox bacterial cells may be exhibited higher viscosity to the sludge surface and easily formed the gel network to aggregate. Large amounts of hydroph...