Tianjin, China
Tianjin, China

Nankai University , often known as Nankai, is a public research university located in Tianjin mainland China. Founded in 1919 by prominent educators Zhang Boling and Yan Fansun , Nankai is one of the most prestigious universities in China. Its alumi include the former Premier Zhou Enlai and Nobel laureates Chen Ning Yang and Tsung-Dao Lee. Wikipedia.

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News Article | May 19, 2017
Site: phys.org

Automobile traffic in this "smart city" should move almost constantly, stopping or slowing as little as possible at traffic lights, on freeway ramps and in traffic circles, says Liu, a Ph.D. candidate in industrial engineering. Likewise, electricity should flow through power lines at an optimal rate, high enough to achieve maximum efficiency but not so high that wires overheat. These streamlined flows of people and power, says Liu, are made possible by machines that process large quantities of data in real time and learn to make intelligent decisions. Liu develops large-scale optimization algorithms, or mathematical models, and applies them to machine learning. His goal is to design a power-transmission system that meets the energy demands of a city with maximum efficiency and minimal cost. Liu recently received the IBM Ph.D. Fellowship Award to study power systems and data analytics at IBM Research-Ireland, one of 12 IBM research laboratories in the world. The fellowship is given each year to 50 outstanding students worldwide. IBM Research-Ireland conducts research into the Cognitive Internet of Things, cognitive integrated healthcare, interactive reasoning, data centric computing and the cloud and privacy. As part of the fellowship program, Liu has been assigned a mentor from IBM Research-Ireland. He will continue his research into power systems and data analytics and will likely spend part of the fall semester in Ireland or at the company's New York site. As an example of machine learning or signal processing, Liu points to E-ZPass, the electronic toll-collection system that serves motorists in the eastern half of the United States and in Ontario, Canada. E-ZPass photographs the license plates of passing cars and employs optimization techniques to read the letters and numbers of license plates, even in blurry photos. Machine learning is also used by online shopping sites such as Amazon to analyze a consumer's purchasing history and recommend similar products that the consumer might be interested in buying. Liu's research focuses on the buses, or intermediate power stations, which take electricity from a power plant and distribute it to homes and businesses in a utility company's service area. He develops models that seek to determine the optimal number, and optimal location, of intermediate stations. He is also working on the development of fast solvers for the popular deep neural networks and other graphical models. A utility company's goal, says Liu, is to transmit electricity from its intermediate stations with maximum efficiency. If a station's wires carry too much electricity, he says, they will exceed the station's voltage limits and overheat, with undesired consequences, shutting down or even igniting. If the wires carry too little electricity, the system loses efficiency. "We want to find a middle point," says Liu. "We can never reach the voltage limit. On the other hand, if the amount of electricity being transmitted is too low, efficiency is reduced. We want to be efficient but not get too close to the limit." A second goal, says Liu, is to solve problems that arise as quickly as possible. "We want to speed up optimization for power networks," he says. "Our goal is to transmit power efficiently while solving problems as quickly as possible." Power grids and other networks generate massive streams of data that must be processed and analyzed in real time. Liu and his group set up polynomial optimization problems (POPs) and solve them by randomly choosing coordinates of data, which can be solved in parallel by multiple machines at the same time to improve efficiency. This contrasts, he says, with the conventional technique, which is called the Newton method and is difficult to parallelize. "The Newton method processes all the data," says Liu, "but it is not possible to do this with a power system because it generates so much data. Our method doesn't process all the data; instead, we pick coordinates, or bits of data, randomly. This greatly reduces the total amount of time needed to solve a problem. "To solve a multidimensional problem using the traditional method took days and did not always yield a feasible solution. With our method, we can arrive at a feasible solution in several minutes to half an hour." Liu and his group reported their results recently in an article titled "Hybrid Methods in Solving Alternating-Current Optimal Power Flows." The article was coauthored with Alan C. Liddell, Jakub Mareček and Martin Takáč. Takáč, an assistant professor of industrial and systems engineering, is Liu's Lehigh Ph.D. adviser. Mareček is with IBM-Ireland, and Liddell is with Notre Dame University. Liu enrolled at Lehigh in 2013 after completing his M.S. in mathematics from the State University of New York at Buffalo. He holds a B.S. in mathematics from Nankai University in Tianjin, China. His other honors include the Dean's Doctoral Assistantship and Dean's Fellowship from Lehigh's P.C. Rossin College of Engineering and Applied Science, the Gotshall Fellowship from Lehigh, and the American Express Machine Learning Contest Award. At Lehigh, Liu is part of a research group called Optimization and Machine Learning (OptML), which includes Takáč; Katya Scheinberg, the Harvey E. Wagner Endowed Chair Professor of Industrial and Systems Engineering; and Frank Curtis, associate professor of industrial and systems engineering. Students in the OptML group receive support to present their work at international conferences. Liu's papers and posters have been accepted at conferences of Neural Information Processing Systems (NIPS), the Institute for Operations Research and the Management Sciences (INFORMS), the International Conference on Machine Learning (ICML); and the Machine Learning Symposium in New York City. Liu and his fellow students are also encouraged to do industrial internships. Liu has completed internships with Siemens Corporate Research and IBM. This summer he will work in Boston with Mitsubishi's Electricity Research Laboratories (MERL) in data analytics. In 2012, he worked with Argonnes National Laboratory near Chicago. "These are amazing opportunities," Liu says. "These are different companies, totally different. These internships connect us to industry. They give us the chance to do something we're interested in and to learn new knowledge at the same time. "The professors in our group are very supportive. They really help us learn how we can contribute and make an impact." Explore further: Scientists propose better battery system for smart home use


News Article | May 19, 2017
Site: www.eurekalert.org

The modern city, says Jie Liu, can be considered a web of networks that should run like a healthy, well-tuned circulatory system. Automobile traffic in this "smart city" should move almost constantly, stopping or slowing as little as possible at traffic lights, on freeway ramps and in traffic circles, says Liu, a Ph.D. candidate in industrial engineering. Likewise, electricity should flow through power lines at an optimal rate, high enough to achieve maximum efficiency but not so high that wires overheat. These streamlined flows of people and power, says Liu, are made possible by machines that process large quantities of data in real time and learn to make intelligent decisions. Liu develops large-scale optimization algorithms, or mathematical models, and applies them to machine learning. His goal is to design a power-transmission system that meets the energy demands of a city with maximum efficiency and minimal cost. Liu recently received the IBM Ph.D. Fellowship Award to study power systems and data analytics at IBM Research-Ireland, one of 12 IBM research laboratories in the world. The fellowship is given each year to 50 outstanding students worldwide. IBM Research-Ireland conducts research into the Cognitive Internet of Things, cognitive integrated healthcare, interactive reasoning, data centric computing and the cloud and privacy. As part of the fellowship program, Liu has been assigned a mentor from IBM Research-Ireland. He will continue his research into power systems and data analytics and will likely spend part of the fall semester in Ireland or at the company's New York site. As an example of machine learning or signal processing, Liu points to E-ZPass, the electronic toll-collection system that serves motorists in the eastern half of the United States and in Ontario, Canada. E-ZPass photographs the license plates of passing cars and employs optimization techniques to read the letters and numbers of license plates, even in blurry photos. Machine learning is also used by online shopping sites such as Amazon to analyze a consumer's purchasing history and recommend similar products that the consumer might be interested in buying. Liu's research focuses on the buses, or intermediate power stations, which take electricity from a power plant and distribute it to homes and businesses in a utility company's service area. He develops models that seek to determine the optimal number, and optimal location, of intermediate stations. He is also working on the development of fast solvers for the popular deep neural networks and other graphical models. A utility company's goal, says Liu, is to transmit electricity from its intermediate stations with maximum efficiency. If a station's wires carry too much electricity, he says, they will exceed the station's voltage limits and overheat, with undesired consequences, shutting down or even igniting. If the wires carry too little electricity, the system loses efficiency. "We want to find a middle point," says Liu. "We can never reach the voltage limit. On the other hand, if the amount of electricity being transmitted is too low, efficiency is reduced. We want to be efficient but not get too close to the limit." A second goal, says Liu, is to solve problems that arise as quickly as possible. "We want to speed up optimization for power networks," he says. "Our goal is to transmit power efficiently while solving problems as quickly as possible." Power grids and other networks generate massive streams of data that must be processed and analyzed in real time. Liu and his group set up polynomial optimization problems (POPs) and solve them by randomly choosing coordinates of data, which can be solved in parallel by multiple machines at the same time to improve efficiency. This contrasts, he says, with the conventional technique, which is called the Newton method and is difficult to parallelize. "The Newton method processes all the data," says Liu, "but it is not possible to do this with a power system because it generates so much data. Our method doesn't process all the data; instead, we pick coordinates, or bits of data, randomly. This greatly reduces the total amount of time needed to solve a problem. "To solve a multidimensional problem using the traditional method took days and did not always yield a feasible solution. With our method, we can arrive at a feasible solution in several minutes to half an hour." Liu and his group reported their results recently in an article titled "Hybrid Methods in Solving Alternating-Current Optimal Power Flows." The article was coauthored with Alan C. Liddell, Jakub Mareček and Martin Takáč. Takáč, an assistant professor of industrial and systems engineering, is Liu's Lehigh Ph.D. adviser. Mareček is with IBM-Ireland, and Liddell is with Notre Dame University. Liu enrolled at Lehigh in 2013 after completing his M.S. in mathematics from the State University of New York at Buffalo. He holds a B.S. in mathematics from Nankai University in Tianjin, China. His other honors include the Dean's Doctoral Assistantship and Dean's Fellowship from Lehigh's P.C. Rossin College of Engineering and Applied Science, the Gotshall Fellowship from Lehigh, and the American Express Machine Learning Contest Award. At Lehigh, Liu is part of a research group called Optimization and Machine Learning (OptML), which includes Takáč; Katya Scheinberg, the Harvey E. Wagner Endowed Chair Professor of Industrial and Systems Engineering; and Frank Curtis, associate professor of industrial and systems engineering. Students in the OptML group receive support to present their work at international conferences. Liu's papers and posters have been accepted at conferences of Neural Information Processing Systems (NIPS), the Institute for Operations Research and the Management Sciences (INFORMS), the International Conference on Machine Learning (ICML); and the Machine Learning Symposium in New York City. Liu and his fellow students are also encouraged to do industrial internships. Liu has completed internships with Siemens Corporate Research and IBM. This summer he will work in Boston with Mitsubishi's Electricity Research Laboratories (MERL) in data analytics. In 2012, he worked with Argonnes National Laboratory near Chicago. "These are amazing opportunities," Liu says. "These are different companies, totally different. These internships connect us to industry. They give us the chance to do something we're interested in and to learn new knowledge at the same time. "The professors in our group are very supportive. They really help us learn how we can contribute and make an impact." Story by Kurt Pfitzer, Editor and Writer, Lehigh University Office of Communications and Public Affairs


News Article | April 17, 2017
Site: cen.acs.org

With the help of a little light, a metal-organic framework (MOF) containing photoluminescent lanthanides can uniquely identify and measure the concentrations of multiple solvents, a research team reports (Chem 2017, DOI: j.chempr.2017.02.010). Applied to a dipstick, the material could enable rapid identification of environmental contaminants on-site rather than require responders to send samples to a lab. The approach could also solve the tricky analytical problem of determining how much H O contaminates D O, an issue for isotopic labeling in biomolecular experiments and calibration for spectroscopic methods such as nuclear magnetic resonance. Led by University of Texas, Austin, chemistry professor Simon M. Humphrey, the team investigated a MOF composed of tris(p-carboxylato)triphenylphosphine and europium(III), gadolinium(III), and terbium(III). Each lanthanide has a unique emission spectrum: EuIII emits red, GdIII emits ultraviolet, and TbIII emits green light. But when solvent molecules bind within the MOF, the molecules’ vibrational frequencies quench the lanthanide emissions in a way that’s unique to each solvent-lanthanide combination. The researchers exploited those properties to identify solvents by creating several MOFs with varying ratios of the lanthanides. They exposed each of four MOF varieties to a solvent, excited them at 365 nm, and compared the resulting emission intensity at three wavelengths. The approach allowed the scientists to identify a characteristic fingerprint for each solvent, thereby distinguishing among H O, D O, methanol, ethanol, toluene, benzene, and 12 other solvents. “We were lucky that we stumbled across the right combination of lanthanides to do this,” Humphrey says. He and colleagues created dipstick-like sensors by using spray glue to deposit the MOFs onto glass slides and immersing them in solvents. The researchers could reuse the dipsticks multiple times by heating them to drive off the solvent. Distinguishing between H O and D O is particularly difficult because the two molecules are so similar. “Using a lanthanide MOF as a sensor to detect a trace amount of H O in D O is really fantastic work that not only greatly deepens the research of luminescent lanthanide MOFs but also proposes a new application,” comments Peng Cheng, a chemistry professor at Nankai University. CORRECTION: This story was updated on April 19, 2017, to correct the spelling of Peng Cheng’s family name.


3-n-Butylphthalide (NBP) has been shown to have protective effects against ischemic stroke. In the present study, we investigated effects of l-3-n-butylphthalide (l-NBP) on the learning and memory impairment induced by chronic cerebral ischemia in rats. Male Wistar rats were administered 20 mg/kg l-NBP by gavage daily for 30 days after the bilateral common carotid artery clamping (two-vessel occlusion, 2-VO). Results showed that daily treatments of 20 mg/kg l-NBP significantly attenuated spatial learning deficits in Morris water maze (MWM) task. Results of long-term potentiation (LTP) indicated that treatment with 20 mg/kg l-NBP attenuated the inhibition of LTP in rat model of 2-VO. Moreover, l-NBP reduced glial fibrillary acidic protein (GFAP)-positive astrocytes induced by chronic cerebral ischemia. The present findings demonstrate the protective effect of l-NBP on chronic cerebral ischemia-induced hippocampus injury, which supports using l-NBP for therapy of cerebral ischemia in the future. Copyright © 2012 Elsevier Ltd. All rights reserved.


Guo D.-S.,Nankai University | Liu Y.,Nankai University
Chemical Society Reviews | Year: 2012

Calixarenes are one kind of phenol-formaldehyde cyclic oligomers, discovered from the Bakelite process. Their intrinsic characteristics, including the unique structural scaffold, facile modification and adjustable inclusion property, show pronounced potential for supramolecular polymerization. In this tutorial review, we summarize the current stage of fabrication of calixarene-based supramolecular polymers. Three types of calixarene-based supramolecular polymers are, respectively, illustrated according to the different activities of calixarenes: (1) calixarene-based supramolecular polycaps, (2) supramolecular polymers with polymeric calixarene scaffolds where the cavities remain unexploited; (3) supramolecular polymers formed by the host-guest interactions offered by calixarene cavities. Furthermore, the stimuli-responsiveness and functions of calixarene-based supramolecular polymers are illustrated, which endow them with a broad range of potential applications as smart, self-healing materials and delivery carriers. This journal is © The Royal Society of Chemistry 2012.


Tang Q.,Nankai University | Zhou Z.,Nankai University
Progress in Materials Science | Year: 2013

Graphene, an atomic monolayer of carbon atoms in a honeycomb lattice realized in 2004, has rapidly risen as the hottest star in materials science due to its exceptional properties. The explosive studies on graphene have sparked new interests towards graphene-analogous materials. Now many graphene-analogous materials have been fabricated from a large variety of layer and non-layer materials. Also, many graphene-analogous materials have been designed from the computational side. Though overshadowed by the rising graphene to some degree, graphene-analogous materials have exceptional properties associated with low dimensionality and edge states, and bring new breakthrough to nanomaterials science as well. In this review, we summarize the recent progress on graphene-analogous low-dimensional materials (2D nanosheets and 1D nanoribbons) from both experimental and computational side, and emphasis is placed on structure, properties, preparation, and potential applications of graphene-analogous materials as well as the comparison with graphene. The reviewed materials include strictly graphene-like planar materials (experimentally available h-BN, silicene, and BC3 as well as computationally predicted SiC, SiC2, B, and B2C), non-planar materials (metal dichalcogenides, metal oxides and hydroxides, graphitic-phase of ZnO, MXene), metal coordination polymers, and organic covalent polymers. This comprehensive review might provide a directional guide for the bright future of this emerging area. © 2013 Elsevier Ltd. All rights reserved.


Hu X.,Nankai University | Zhou Q.,Nankai University
Chemical Reviews | Year: 2013

The article examines the health and ecosystem risks of graphene. The size of graphene directly controls the physicochemical properties of graphene. A larger graphene size induces a smaller percolation threshold with changes of the thermal and mechanical properties of graphene. The thermal conductivity of graphene grows with increasing linear dimensions of graphene flakes. A strong size dependence of charge distributions was found in rectangular graphene sheets. The cytotoxicity, subcellular localization, blood circulation, organ uptake, and etiopathology of nanomaterials are affected by the size distribution. Importantly, the size distribution influences the nanotoxicity because the exposure dose of nanomaterials is related to mass and sizes in toxicology. The previous work has demonstrated that nanomaterials with a certain size distribution exhibited the greatest cytotoxicity and cellular uptake. Defects such as structural imperfections and chemical impurities could unintentionally or unavoidably produce edges that disturb the reactive microenvironment and paths of bioresponses.


Guo D.-S.,Nankai University | Liu Y.,Nankai University
Accounts of Chemical Research | Year: 2014

ConspectusDevelopments in macrocyclic chemistry have led to supramolecular chemistry, a field that has attracted increasing attention among researchers in various disciplines. Notably, the discoveries of new types of macrocyclic hosts have served as important milestones in the field. Researchers have explored the supramolecular chemistry of several classical macrocyclic hosts, including crown ethers, cyclodextrins, calixarenes, and cucurbiturils. Calixarenes represent a third generation of supramolecular hosts after cyclodextrins and crown ethers. Easily modified, these macrocycles show great potential as simple scaffolds to build podand-like receptors. However, the inclusion properties of the cavities of unmodified calixarenes are not as good as those of other common macrocycles. Calixarenes require extensive chemical modifications to achieve efficient endo-complexation.p-Sulfonatocalix[n]arenes (SCnAs, n = 4-8) are a family of water-soluble calixarene derivatives that in aqueous media bind to guest molecules in their cavities. Their cavities are three-dimensional and π-electron-rich with multiple sulfonate groups, which endow them with fascinating affinities and selectivities, especially toward organic cations. They also can serve as scaffolds for functional, responsive host-guest systems. Moreover, SCnAs are biocompatible, which makes them potentially useful for diverse life sciences and pharmaceutical applications.In this Account, we summarize recent work on the recognition and assembly properties unique to SCnAs and their potential biological applications, by our group and by other laboratories. Initially examining simple host-guest systems, we describe the development of a series of functional host-guest pairs based on the molecular recognition between SCnAs and guest molecules. Such pairs can be used for fluorescent sensing systems, enzymatic activity assays, and pesticide detoxification. Although most macrocyclic hosts prevent self-aggregation of guest molecules, SCnAs can induce self-aggregation. Researchers have exploited calixarene-induced aggregation to construct supramolecular binary vesicles. These vesicles respond to internal and external stimuli, including temperature changes, redox reactions, additives, and enzymatic reactions. Such structures could be used as drug delivery vehicles.Although several biological applications of SCnAs have been reported, this field is still in its infancy. Continued exploration of the supramolecular chemistry of SCnAs will not only improve the existing biological functions but also open new avenues for the use of SCnAs in the fields of biology, biotechnology, and pharmaceutical research. In addition, we expect that other interdisciplinary research efforts will accelerate developments in the supramolecular chemistry of SCnAs. © 2014 American Chemical Society.


Cheng F.,Nankai University | Chen J.,Nankai University
Chemical Society Reviews | Year: 2012

Because of the remarkably high theoretical energy output, metal-air batteries represent one class of promising power sources for applications in next-generation electronics, electrified transportation and energy storage of smart grids. The most prominent feature of a metal-air battery is the combination of a metal anode with high energy density and an air electrode with open structure to draw cathode active materials (i.e., oxygen) from air. In this critical review, we present the fundamentals and recent advances related to the fields of metal-air batteries, with a focus on the electrochemistry and materials chemistry of air electrodes. The battery electrochemistry and catalytic mechanism of oxygen reduction reactions are discussed on the basis of aqueous and organic electrolytes. Four groups of extensively studied catalysts for the cathode oxygen reduction/evolution are selectively surveyed from materials chemistry to electrode properties and battery application: Pt and Pt-based alloys (e.g., PtAu nanoparticles), carbonaceous materials (e.g., graphene nanosheets), transition-metal oxides (e.g., Mn-based spinels and perovskites), and inorganic-organic composites (e.g., metal macrocycle derivatives). The design and optimization of air-electrode structure are also outlined. Furthermore, remarks on the challenges and perspectives of research directions are proposed for further development of metal-air batteries. © The Royal Society of Chemistry 2012.


Jin Z.,Nankai University
Natural Product Reports | Year: 2011

A great number of structurally diverse natural products containing five-membered heterocyclic subunits, such as imidazole, oxazole, thiazole, and their saturated congeners, are abundant in nature. These naturally occurring metabolites often exhibit extensive and pharmacologically important biological activities. The latest progress in the isolation, biological activities, chemical synthetic studies, and biosynthetic pathways on these natural products is summarized in this review. © 2011 The Royal Society of Chemistry.

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