Tsinghua University
  • Beijing, China
Recent publications
Microbial activity and regrowth in drinking water distribution systems is a major concern for water service companies. However, previous studies have focused on the microbial composition and diversity of the drinking water distribution systems (DWDSs), with little discussion on microbial molecular ecological networks (MENs) in different water supply networks. MEN analysis explores the potential microbial interaction and the impact of environmental stress, to explain the characteristics of microbial community structures. In this study, the random matrix theory-based network analysis was employed to investigate the impact of seasonal variation including water source switching on the networks of three DWDSs that used different disinfection methods. The results showed that microbial interaction varied slightly with the seasons but was significantly influenced by different DWDSs. Proteobacteria, identified as key species, play an important role in the network. Combined UV-chlorine disinfection can effectively reduce the size and complexity of the network compared to chlorine disinfection alone, ignoring seasonal variations, which may affect microbial activity or control microbial regrowth in DWDSs. This study provides new insights for analyzing the dynamics of microbial interactions in DWDSs.
With the continuous development of nanomaterials in recent years, the application of nanocatalysts in catalytic ozone oxidation has attracted more and more researchers' attention due to their excellent catalytic properties. In this review, we systematically summarized the current research status of nanocatalysts mainly involving material categories, mechanisms and catalytic efficiency. Based on summary and analysis, we found most of the reported nanocatalysts were in the stage of laboratory research, which was caused by the nanocatalysts defects such as easy aggregation, difficult separation, and easy leakage. These defects might result in severe resource waste, economic loss and potentially adverse effects imposed on the ecosystem and human health. Aiming at solving these defects, we further analyzed the reasons and the existing reports, and revealed that coupling nano-catalyst and membrane, supported nanocatalysts and magnetic nanocatalysts had promising potential in solving these problems and promoting the actual application of nanocatalysts in wastewater treatment. Furthermore, the advantages, shortages and our perspectives of these methods are summarized and discussed.
Predicting the logarithm of hexadecane/air partition coefficient (L) for organic compounds is crucial for understanding the environmental behavior and fate of organic compounds and developing prediction models with polyparameter linear free energy relationships. Herein, two quantitative structure activity relationship (QSAR) models were developed with 1272 L values for the organic compounds by using multiple linear regression (MLR) and support vector machine (SVM) algorithms. On the basis of the OECD principles, the goodness of fit, robustness and predictive ability for the developed models were evaluated. The SVM model was first developed, and the predictive capability for the SVM model is slightly better than that for the MLR model. The applicability domain (AD) of these two models has been extended to include more kinds of emerging pollutants, i.e., oraganosilicon compounds. The developed QSAR models can be used for predicting L values of various organic compounds. The van der Waals interactions between the organic compound and the hexadecane have a significant effect on the L value of the compound. These in silico models developed in current study can provide an alternative to experimental method for high-throughput obtaining L values of organic compounds.
Over the past decade, the emission standards and fuel standards in Beijing have been upgraded twice, and the vehicle structure has been improved by accelerating the elimination of 2.95 million old vehicles. Through the formulation and implementation of these policies, the emissions of carbon monoxide (CO), volatile organic compounds (VOCs), nitrogen oxides (NOx), and fine particulate matter (PM2.5) in 2019 were 147.9, 25.3, 43.4, and 0.91 kton in Beijing, respectively. The emission factor method was adopted to better understand the emissions characteristics of primary air pollutants from combustion engine vehicles and to improve pollution control. In combination with the air quality improvement goals and the status of social and economic development during the 14th Five-Year Plan period in Beijing, different vehicle pollution control scenarios were established, and emissions reductions were projected. The results show that the emissions of four air pollutants (CO, VOCs, NOx, and PM2.5) from vehicles in Beijing decreased by an average of 68% in 2019, compared to their levels in 2009. The contribution of NOx emissions from diesel vehicles increased from 35% in 2009 to 56% in 2019, which indicated that clean and energy-saving diesel vehicle fleets should be further improved. Electric vehicle adoption could be an important measure to reduce pollutant emissions. With the further upgrading of vehicle structure and the adoption of electric vehicles, it is expected that the total emissions of the four vehicle pollutants can be reduced by 20%-41% by the end of the 14th Five-Year Plan period.
Semiconductor photocatalytic technology has shown great prospects in converting solar energy into chemical energy to mitigate energy crisis and solve environmental pollution problems. The key issue is the development of high-efficiency photocatalysts. Various strategies in the state-of-the-art advancements, such as heterostructure construction, heteroatom doping, metal/single atom loading, and defect engineering, have been presented for the graphitic carbon nitride (g-C3N4)-based nanocomposite catalysts to design their surface chemical environments and internal electronic structures to make them more suitable for different photocatalytic applications. In this review, nanoarchitecture design, synthesis methods, photochemical properties, potential photocatalytic applications, and related reaction mechanisms of the modified high-efficiency carbon nitride-based photocatalysts were briefly summarized. The superior photocatalytic performance was identified to be associated with the enhanced visible-light response, fast photoinduced electron-hole separation, efficient charge migration, and increased unsaturated active sites. Moreover, the further advance of the visible-light harvesting and solar-to-energy conversions are proposed.
Hyaline cartilage plays a critical role in maintaining joint function and pain. However, the lack of blood supply, nerves, and lymphatic vessels greatly limited the self-repair and regeneration of damaged cartilage, giving rise to various tricky issues in medicine. In the past 30 years, numerous treatment techniques and commercial products have been developed and practiced in the clinic for promoting defected cartilage repair and regeneration. Here, the current therapies and their relevant advantages and disadvantages will be summarized, particularly the tissue engineering strategies. Furthermore, the fabrication of tissue-engineered cartilage under research or in the clinic was discussed based on the traid of tissue engineering, that is the materials, seed cells, and bioactive factors. Finally, the commercialized cartilage repair products were listed and the regulatory issues and challenges of tissue-engineered cartilage repair products and clinical application would be reviewed.
Hybrid mobile robots, which combine the advantages of serial and parallel robots and have the ability to realize processing in situ, have considerable application potential in the field of processing and manufacturing. In this paper, a hybrid mobile robot used for wind turbine blade polishing is presented. The robot combines an automated guided vehicle, a 2-DoF robotic arm, and a 3-RCU parallel module. To improve the accuracy, investigating the elasto-geometrical calibration of the robot is necessary. Considering that the 3-RCU parallel module has weak stiffness along the gravitational direction, the stiffness model was established to estimate the deformation caused by the gravity of the mobile platform, ball screws, and motors. Subsequently, a rigid-flexible coupling error model considering structural and stiffness parameter errors is established. Based on these, a parameter identification method for the simultaneous identification of structural and stiffness parameter errors is proposed herein. For the 2-DoF robotic arm with parallelogram mechanisms, an intuitive error model considering the posture error caused by the parallelogram mechanism errors is established. The regularized nonlinear least squares method was adopted for parameter identification. Thereafter, a compensation strategy for the hybrid mobile robot that comprehensively considers the pose errors of the 3-RCU parallel module and 2-DoF robotic arm is proposed. Finally, a verification experiment was performed on the prototype, and the results indicated that after elasto-geometrical calibration, the maximum/mean of the position and posture errors of the hybrid mobile robot decreased from 3.738 mm/2.573 mm to 0.109 mm/0.063 mm and 0.236°/0.179° to 0.030°/0.013°, respectively. Owing to the decrease in the robot pose errors, the quality of the polished surface was more uniform. The range and standard deviation of roughness distribution of the polished surface were reduced from 0.595 μm and 0.248 μm to 0.397 μm and 0.127 μm. The methods proposed herein have reference significance for elasto-geometrical calibration of other parallel or hybrid robots.
In marine operations, the performance of model-based automatic control design and decision support systems highly relies on the accuracy of the representative mathematical models. Model fidelity can be crucial for safe voyages and offshore operations. This paper proposes a data-driven parametric model identification of a ship with 6 degrees of freedom (6DOF) exposed to waves using sparse regression according to the vessel motion measurements. The features of the complex ship dynamics are extracted and expressed as a linear combination of several functions. Thruster inputs and environmental loads are considered. The hydrodynamic coefficients and wave-induced loads are simultaneously estimated. Unlike earlier studies using a limited number of unknown functions, a library of abundant candidate functions is applied to fully consider the coupling effects among all DOFs. The benefit of the proposed method is that it does not require the exact construction of the library functions. Based on the estimated model, short-term motion prediction is achievable. The algorithm is verified through experiments. The method can be extended to other types of floating structures.
Additive manufacturing has received attention for the fabrication of medical implants that have customized and complicated structures. Biodegradable Zn metals are revolutionary materials for orthopedic implants. In this study, pure Zn porous scaffolds with diamond structures were fabricated using customized laser powder bed fusion (L-PBF) technology. First, the mechanical properties, corrosion behavior, and biocompatibility of the pure Zn porous scaffolds were characterized in vitro. The scaffolds were then implanted into the rabbit femur critical-size bone defect model for 24 weeks. The results showed that the pure Zn porous scaffolds had compressive strength and rigidity comparable to those of cancellous bone, as well as relatively suitable degradation rates for bone regeneration. A benign host response was observed using hematoxylin and eosin (HE) staining of the heart, liver, spleen, lungs, and kidneys. Moreover, the pure Zn porous scaffold showed good biocompatibility and osteogenic promotion ability in vivo. This study showed that pure Zn porous scaffolds with customized structures fabricated using L-PBF represent a promising biodegradable solution for treating large bone defects.
The high demand for rapid wound healing has spurred the development of multifunctional and smart bioadhesives with strong bioadhesion, antibacterial effect, real-time sensing, wireless communication, and on-demand treatment capabilities. Bioadhesives with bio-inspired structures and chemicals have shown unprecedented adhesion strengths, as well as tunable optical, electrical, and bio-dissolvable properties. Accelerated wound healing has been achieved via directly released antibacterial and growth factors, material or drug-induced host immune responses, and delivery of curative cells. Most recently, the integration of biosensing and treatment modules with wireless units in a closed-loop system yielded smart bioadhesives, allowing real-time sensing of the physiological conditions (e.g., pH, temperature, uric acid, glucose, and cytokine) with iterative feedback for drastically enhanced, stage-specific wound healing by triggering drug delivery and treatment to avoid infection or prolonged inflammation. Despite rapid advances in the burgeoning field, challenges still exist in the design and fabrication of integrated systems, particularly for chronic wounds, presenting significant opportunities for the future development of next-generation smart materials and systems.
Labeling of mesenchymal stem cells (MSCs) with superparamagnetic iron oxide nanoparticles (SPIONs) has emerged as a potential method for magnetic resonance imaging (MRI) tracking of transplanted cells in tissue repair studies and clinical trials. Labeling of MSCs using clinically approved SPIONs (ferumoxytol) requires the use of transfection reagents or magnetic field, which largely limits their clinical application. To overcome this obstacle, we established a novel and highly effective method for magnetic labeling of MSC spheroids using ferumoxytol. Unlike conventional methods, ferumoxytol labeling was done in the formation of a mechanically tunable biomimetic hydrogel-induced MSC spheroids. Moreover, the labeled MSC spheroids exhibited strong MRI T2 signals and good biosafety. Strikingly, the encapsulated ferumoxytol was localized in the extracellular matrix (ECM) of the spheroids instead of the cytoplasm, minimizing the cytotoxicity of ferumoxytol and maintaining the viability and stemness properties of biomimetic hydrogel-induced MSC spheroids. This demonstrates the potential of this method for post-transplantation MRI tracking in the clinic.
Although nickel-titanium (NiTi) alloys have many excellent material properties, micro structures of NiTi alloys are hardly be machined by conventional mechanical machining due to its high elasticity and severe tool wear. A micro tube as an active catheter with typical driving structures made from NiTi shape memory alloy (SMA) is a key part in an interventional micro robot system. The driving structures, as complex 3D micro structures pierced through a thin-walled micro tube, are usually fabricated by a lithography-based process. However, not only the process is low efficiency and costly, but also its etching depth is limited. In this chapter, a novel process of SS-3D micro EDM with the movement of two-axis linkage and one-axis servo is applied in efficiently machining the 3D micro structures. The processing procedures are presented in details including 3D model design, scanning paths planning, 3D NC codes generation, and 3D micro EDM. The emphasis is focused on the methods for planning scanning paths and generating CNC codes considering the special 3D micro structures. Using the planned complementary scanning paths, the processing stability is improved for successfully machining the typical driving structures within 5 h. The machining experiments verify the feasibility of the SS-3D micro EDM process and related methods.
Bamboo is a material with great potential in the design and application of low-carbon building, but the current discussion mostly focuses on the material level rather than the life cycle. In this study, a bamboo building unit in Guangzhou, China is studied for the carbon emission of building materials and building operation. On this basis, the design of bamboo construction is optimized with the aim of improving the life cycle “carbon efficiency”. Among the 60 construction schemes, the carbon emission proportion of building materials accounts for 5.0%–13.1% of the total, and building operation is the key stage to improve the carbon efficiency. For construction optimization, the 3-layer construction type has better carbon efficiency than the 2-layer type. As for the framework, the difference between BPB and BMB as interlayer board can be ignored, and the combination of BPB/BMB and indoor plaster shows better carbon efficiency than the BSB/BFB as inner board. As for the core cavity, the arrangement of infill and air layer can effectively improve the carbon efficiency, and the use of non-hygroscopic inorganic materials is more advantageous than the hygroscopic organic materials under the local climate conditions.
Critical services such as health care, finance, power, public utility, are being hosted in critical computing systems like cloud, big data and AI platforms. Compliance is one of the main instruments governments exert on such computing systems for regulating security and reliability so that governments and the public can obtain the assurance that no severe unacceptable impacts or incidents may occur. This article gives a comprehensive discussion on the compliance problem in critical computing systems, and describes state-of-the-art technologies and practices of compliance validation/enforcement. This article is very helpful for those professionals working on critical computing systems and services.
With the rapid growth of the wireless network scale and the aggressive development of communication technology, the communication network connection is required to drift to digits in order to ameliorate the network efficiency. Digital twin (DT) is one of the most promising techniques, which promotes the digital transition of communication networks by establishing mappings between virtual models and physical objects. Nevertheless, due to the limitation and heterogeneity of equipment resources, it is a great challenge to provide efficient network resource allocation. To solve this problem, the authors propose a novel network paradigm based on digital twin to build the topology and model of the communication system. Then a distributed deep reinforcement learning (DRL) method is designed to dispose the problem of resource allocation in cellular networks, and an online–offline learning framework is proposed. Firstly, the offline training is carried out in the simulation environment, and the DRL algorithm is applied to train the deep neural network (DNN). Secondly, in the process of online learning, the real data are further utilized to fine-tune the DNN. Numerical results illustrate the superiority of the proposed method in terms of average system capacity. In the case of different user densities, the performance of the proposed algorithm has more advantages than that of benchmark algorithms and has better generalization ability.
Distribution function of relaxation times (DRT or DFRT) is a well-established method of solving electrochemical impedance spectroscopy (EIS) data from electrochemical component, such as fuel cells and battery. It can be used to develop the most accurate equivalent circuit models without the input of any circuit architecture information, due to the complexity in equivalent circuit model (ECM). For the first time, this paper implemented DRT methods in polymer electrolyte membrane water electrolyzers (PEMECs). The regularization parameter, characteristic parameters and frequency factors of the solving process are introduced. The results show that (1) DRT method represents the original impedance spectrum of hydrogen production in the form of three characteristic peaks, which correspond to the main polarization processes in hydrogen production system, including water/gas diffusion, hydrogen evolution reaction (HER), oxygen evolution reaction (OER) reactions, and proton transfer processes. (2) The optimal regularization parameter is 0.01 for different current and temperature. (3) The frequency of water/gas diffusion, HER /OER reactions and proton transfer processes is 0.1–60 Hz, 100–1000 Hz and 2000–4000 Hz, respectively. (4) DRT methods couples with genetic algorithm can effectively identify various types of polarization impedance corresponding to different characteristic frequencies without equivalent circuit model.
A novel machine learning model, eXtreme Gradient Boosting (XGBoost), was used for the purpose of evaluating the moment capacity of cold-formed steel (CFS) channel beams with edge-stiffened web holes subject to bending. A total of 1,620 data points were generated for training the XGBoost model, using an elasto-plastic finite element model which was validated against 12 sets of test data taken from the existing literature. The R2 score of XGBoost predictions for the moment capacity was around 99%. The performance of current design equations was evaluated through the comparison of their results against those obtained from the XGBoost model. The moment capacities obtained from the XGBoost testing dataset were also compared with that determined from the existing design equations for un-stiffened holes (USH) and edge-stiffened holes (ESH). The moment capacities determined from the current design equations for USH and ESH were found to be excessively conservative by 38.3%, and unconservative by 36.2% on average, respectively. Therefore, new design equations were proposed based on the results of parametric study using the XGBoost model. In the detailed parametric analysis, the effects of web depth, section thickness, and beam length on the moment capacity of channel beams (CFSCB) with ESH were considered. From the results of XGBoost outputs, the absolute percentage error of new design equations for that based on the strengths of unperforated CFSCB was 8.78%, and for that based on the strengths of CFSCB with USH, the absolute percentage error was 13.7%. Additionally, a reliability analysis was performed to evaluate the accuracy of the proposed equations that were used to predict the moment capacity of CFS channel beams with ESH subject to bending. The reliability indices of all the proposed equations were greater than 2.5 which can be reliable as per the guidelines of AISI.
Institution pages aggregate content on ResearchGate related to an institution. The members listed on this page have self-identified as being affiliated with this institution. Publications listed on this page were identified by our algorithms as relating to this institution. This page was not created or approved by the institution. If you represent an institution and have questions about these pages or wish to report inaccurate content, you can contact us here.
32,645 members
Jun Xu
  • Department of Chemical Engineering
Alexander Kovalev
  • Department of Engineering Mechanics
Bin Zhao
  • Department of Building Science, School of Architecture
Zheng Yao
  • Department of Electronic Engineering
Information
Address
Qinghua Yuan #1, 100084, Beijing, China
Head of institution
Yong Qiu
Website
http://www.tsinghua.edu.cn/publish/newthuen/index.html
Phone
+86-10-62781779
Fax
+86-10-62781779