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The MSU Foundation's ongoing economic development initiatives focus on taking Michigan State University's faculty and researcher technologies to market, investing in MSU student entrepreneurs, and working with area partners to build and grow the region's robust, thriving culture of innovation. "Our mission at Renaissance is to serve as a bridge between researchers, entrepreneurs, venture capitalists, and major corporations in Michigan," said Chris Rizik, chief executive officer of Renaissance Venture Capital Fund. "We are impressed with the growth of innovation efforts at Michigan State University and are excited to extend our presence and network in the region." The TIC, managed and operated by the MSU Foundation, offers its tech-based members office space, programmatic support, and resources aimed at helping startups and early-stage companies flourish. The TIC is adjacent to the MSU Innovation Center. About the Renaissance Venture Capital Fund The Renaissance Venture Capital Fund is a fund of funds that supports the growth of venture capital in Michigan while serving as a bridge between Michigan's emerging innovation company community and its strong industrial and commercial base. Formed by Business Leaders for Michigan, the Renaissance Venture Capital Fund boasts as its members many of Michigan's most important organizations. It has become a national model for strategic, financially successful regional investing. Through its investment in top tier venture firms that are active in Michigan, as well as its own co-investments in emerging Michigan companies, the Renaissance Venture Capital Fund is helping to drive forward both innovation and growth of emerging companies in the region. And it is again proving that Michigan, with its unique combination of scientific, engineering and business talent, is a great place in which to invest. For more information, please visit: www.renvcf.com. About the Michigan State University Foundation Established in 1973 as an independent, non-profit corporation, the Michigan State University Foundation fuels economic development initiatives through the commercialization of cutting-edge technologies invented by Michigan State University faculty, staff, and students. At its core is an extensive program, focusing on the support of research, invention, and entrepreneurship. The Michigan State University Foundation operates Michigan Biotechnology Institute, Red Cedar Ventures, Spartan Innovations, and the University Corporate Research Park. Further, the Foundation manages and operates the East Lansing Technology Innovation Center. More information on the Foundation's notable achievements, provided services, key leadership, and history are available at www.msufoundation.org About the East Lansing Technology Innovation Center Founded in 2008, right in the heart of downtown East Lansing, the East Lansing Technology Innovation Center, also known as the TIC, became the first business incubator in the region. Today, the space continues to be home to technology startup companies, offering them support and space to grow their ideas. Members have direct access to resources within the MSU Innovation Center, as well as Michigan State University's campus. Connecting members with a vast network of area professionals, community resources, and venture capitalists, the TIC offers the space for tech entrepreneurs to explore their ideas, take creative risks, and grow their networks. For more about the East Lansing Technology Innovation Center, please visit: www.eastlansingtic.org. To view the original version on PR Newswire, visit:http://www.prnewswire.com/news-releases/renaissance-venture-capital-fund-expands-to-east-lansing-technology-innovation-center-300454760.html


News Article | May 22, 2017
Site: www.greencarcongress.com

« Daimler lays foundation for its second battery factory; €500M investment for premium eBattery plant | Main | Tritium trials Veefil 50 kW fast chargers in China in joint venture with Oxford University Innovation » The Professor Ferdinand Porsche Prize was awarded by Vienna University of Technology to Anke Kleinschmit, Head of Corporate Research & Sustainability and Environmental Officer for Daimler AG, for the development of the innovative exhaust gas aftertreatment system in the new OM 654 four-cylinder diesel engine (earlier post). The went into series production in 2016 and stands out for the fact that NO emissions stay low in real operating conditions. The automotive engineering prize is awarded once every two years to people who have made a significant contribution to the development of the motor vehicle with their innovation(s). The four-cylinder diesel engine, developed under the leadership of Bernhard Heil, is designed to meet future emissions legislation (RDE – Real Driving Emissions) and stands out for its exemplary efficiency and low NO emissions. This is made possible by, among other features, a newly-developed stepped-bowl combustion process; exhaust treatment technologies configured directly on the engine together with multiway exhaust gas recirculation using cooled high-pressure and low-pressure technology. This innovative technology package significantly reduces the engine’s untreated emissions across all characteristics. The four-cylinder diesel engine has already earned a good reputation for its performance out on the road. The engineers for the industry magazine”. And ADAC commented , after road tests of the diesel engine: “


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 | May 25, 2017
Site: www.eurekalert.org

The winners of the students' competition 'Capture the Flag' organized by Siemens LLC with the support of Peter the Great St. Petersburg Polytechnic University, were awarded On May 24, at the Plenary session of the International Polytechnic Week, the winners of the students' competition "Capture the Flag" organized by Siemens LLC with the support of Peter the Great St. Petersburg Polytechnic University (SPbPU), were awarded. Siemens is a long-term strategic partner of Polytechnic University. Head of Corporate Research & Technology and Vice President of Siemens LLC Dr. Martin Gitsels and Vice-Rector for International Relations Mr. Dmitry Arseniev presented memorable gifts to the best teams, as well as certificates for scholarships from Siemens. More than sixty students from various universities of St. Petersburg participated in the competition. Altogether, 11 teams competed in the framework of the event, 8 of them successfully reached the finals. The competition was opened by Mr. Sergei Sobolev, Head of research, Corporate Research & Technology Siemens LLC. He greeted the team and described the rules of the event. "Catch the Flag" competition is a new option of hackathon where the teams can choose the tasks of various level of complexity and accordingly receive a different number of points for problem solving. The winner is the team that gets the largest number of points. The tasks of the competition were divided into two types: a quiz and tasks requiring comprehensive engineering solutions. In addition, the participants had an opportunity to solve business cases and present the results to the experts of Siemens. Additional points were given to those teams who successfully presented their solutions in English, and also undertook tasks from various professional fields. Subjects of the tasks were provided by Siemens LLC and included the following areas: programming, power engineering, electrical engineering, transport systems, production automation. "The competition gave me a very positive impression. A chance to solve real-life industrial tasks was useful practical experience. In addition, I met different people able to solve interesting engineering problems," says Eugene Moutin, a student of SPbPU. " The most fierce struggle started on the second day of the event. By that time, all teams had built the strategy of their work and were presenting new solutions to experts till the end of the competition. Within two days the teams managed to solve more than 30 tasks and cases of Siemens. A team of five students from Peter the Great St. Petersburg Polytechnic University called "The Sons of Zeus" won the competition. Participants were Dmitry Eliseev, Mikhail Petrov, Kamil Timraleyev, Alexander Frolov and Vadim Yaparov. "Good training at the Institute of Energy and Transport Systems SPbPU helped us to win this competition. Our team turned out to be very sophisticated in terms of the diversity of competencies, but also, of course, a great desire to have an internship in Siemens helped us reach success," says the participant of the SPbPU team student Kamil Timraleyev.


MIDLAND, MI, February 17, 2017-- Bernard J. Meister has been included in Marquis Who's Who. As in all Marquis Who's Who biographical volumes, individuals profiled are selected on the basis of current reference value. Factors such as position, noteworthy accomplishments, visibility, and prominence in a field are all taken into account during the selection process.Bernard J. Meister was born to Benjamin and Gertrude Meister in February, 1941 in Maynard, Massachusetts. He attended Maynard public schools and graduated as valedictorian in 1958. He entered Worchester Polytechnic Institute in 1958 and earned a Bachelor of Science in Chemical Engineering in 1962. Bernie then entered Cornell University, studied chemical engineering and polymers and wrote his thesis on the breakup of jets into droplets for immiscible liquid systems. He obtained his PhD in 1966.In April 1966 Dr. Meister joined the Dow Chemical Company in the Corporate Research Lab in Midland, Michigan. He started in the rheology lab measuring high shear rate normal stresses in elastic liquids, and using the data to help formulate a viscoelastic constitutive equation that could be used to simulate polymer processing equipment. He then used the constitutive equation he developed to simulate the injection molding of polystyrene cups. In 1972 Bernie married Janet White and had now transferred to the styrenics research lab and he began working on the simulation of the polystyrene polymerization process. He used the kinetic equations of chain initiation, chain propagation, chain termination and chain transfer to predict the amount of polystyrene produced with time and its molecular weight and molecular weight distribution. This computer program was used to guide the development of a new process for a new polystyrene with high molecular weight and low oligomers at high production rates. This was accomplished, the production plant was built and started up on prime product in 1976. The molecular simulation was installed on the plant and started up concurrently with the process. Dr. Meister then added rubberized feed, two phase polymerization and grafting to the program so high impact polystyrene products could be developed in the same way. Also styrene acrylonitrile copolymerization was added for the development of mass ABS products. This made it possible for the researchers and manufacturers to run off line simulations to modify plant conditions and develop new products.The large number of successes and cost savings attributed to the molecular kinetics program led to the promotion of Dr. Meister to Associate Scientist in 1983. Also in that year, he was named Chemical Engineer of the Year by the American Institute of Chemical Engineers. On becoming a scientist, there is less time spent on individual products and more time on guiding people and project development and technical review of projects in other locations. One project he guided was the development of a new high impact polystyrene process that provided more grafting. During this time he also refocused on a product design program that could lead to a similar success to the polymerization programs. The focus was on entangling and disentangling molecules and how it was affected by the velocity fields in processing equipment. Bernie was promoted to Research Scientist in 1992 and retired in 1999. He continued to take on projects for Dow part time under contract until 2005. Throughout his lengthy Dow career, he wrote over 100 Dow reports and also contributed his knowledge to many articles in professional journals. Dr. Meister maintains his membership with AIChE, ACS, SPE, Sigma Xi, and the Society of Rheology.About Marquis Who's Who :Since 1899, when A. N. Marquis printed the First Edition of Who's Who in America , Marquis Who's Who has chronicled the lives of the most accomplished individuals and innovators from every significant field of endeavor, including politics, business, medicine, law, education, art, religion and entertainment. Today, Who's Who in America remains an essential biographical source for thousands of researchers, journalists, librarians and executive search firms around the world. Marquis now publishes many Who's Who titles, including Who's Who in America , Who's Who in the World , Who's Who in American Law , Who's Who in Medicine and Healthcare , Who's Who in Science and Engineering , and Who's Who in Asia . Marquis publications may be visited at the official Marquis Who's Who website at www.marquiswhoswho.com


News Article | November 14, 2016
Site: phys.org

Our future is likely to rely on many 'systems of systems' - networks of technical operations, that work independently, but need to act together. Creating conditions for all sorts of systems to work together could be the next step in optimising technological efficiency. Ending in September 2016, the EU-funded DYMASOS (Dynamic Management of Physically Coupled Systems of Systems) project has developed new management methods and engineering tools for these 'cyber-physical' systems of systems. Improved management leads to better performance and could significantly reduce our consumption of resources and carbon footprints. 'The project has made an important contribution in taking first concrete steps into realising and concretising a novel field of research - the Internet of Things,' says Dr. Iiro Harjunkoski, from ABB Corporate Research in Germany and a member of the DYMASOS consortium. This will enable everyday objects to be networked via the internet, allowing them to send and receive data and giving any system the capacity to be 'smart' and coordinate with other systems. DYMASOS was based on real industrial case studies. These were underpinned by a thorough analysis of markets, industrial needs, and challenges of the industrial project partners.'The research was steered by the application cases but nonetheless also geared towards obtaining fundamental results and new insights.' explains project co-ordinator, Professor Sebastian Engell of Technische Universität Dortmund. The focus of the case studies were in the fields of chemical production, from companies, BASF and INEOS, both among the largest chemicals producers in the world, and in the operation and engineering of electric power distribution and electric vehicle charging infrastructures, using data from HEP ODS, Croatia, and AYESA, Spain. 'The realistic modelling and simulation of DYMASOS is one of the critical issues addressed by the project,' says Dr Patrick Panciatici, Scientific Advisor at RTE, France. DYMASOS developed four different approaches to modelling systems of systems. In a comparison to the behaviour of biological systems, ETH Zurich looked at understanding and controlling population behaviour. They looked, for example at modelling the overnight recharging behaviour of electric car owners, knowing only information about average population behaviour. An electric vehicle case study from the city of Malaga carried out by the University of Seville, modelled coalitional control - how to jointly optimise the behaviour of different elements in a process. TU Dortmund also modelled market-like mechanisms that try to optimise results by dynamic price-setting or constraining resources to balance supply and demand; this was applied to a petrochemical site of INEOS in Cologne and a reactor system at BASF. The University of Zagreb developed a hierarchical control model; where the grid configuration can change dynamically to minimise power losses, based on an electric distribution grid case study provided by HEP ODS. Large-scale simulations of these complex systems successfully validated the management and control algorithms produced. The DYMASOS Engineering Platform provides guidelines for the design of evolving systems of systems that can balance local autonomy and global management. DYNAMOS member, Mark Lewis, a Low Carbon Consultant at Tees Valley Unlimited, in the UK says,'the project has developed a number of practical demonstrations which will interest other complexes within and across companies and organisations to start to take further interest.' Industrial project members are now implementing the solutions developed by DYMASOS and this will give European operators of large technical systems and providers of management and automation solutions strategic competitive advantages, including cost savings, energy efficiency, higher stability and improved resilience to faults and changes in demand. Explore further: Solutions for an Internet of energy


News Article | November 14, 2016
Site: www.prweb.com

Phronesis Partners, a global Research & Consulting company, announced the appointment of Ashish Nayyar as its SVP for Financial and Business Research practice. Ashish brings with him over 15 years of rich experience in Financial & Business Research outsourcing, Management Consulting and client development. He has an expertise in innovative client solutioning, assuring superlative quality delivery and establishing high performance teams. Ashish has built over 50 research teams for various large corporates, global private equity firms, top investment banks and many leading consulting firms across a range of functions such as corporate strategy, sales & marketing, procurement, deal sourcing, due diligence, portfolio monitoring and corporate finance, amongst others. In his previous assignment as Director and Global Head of Private Equity & Corporate Research at Copal Amba- a Moody’s Analytics company, serving it for over 8 years, Ashish was instrumental in establishing and driving growth in several new business lines for the organization. Prior to this, he was a founding member of Four-S Services, a boutique financial advisory firm. Ashish also played a key role in some of the critical projects relating to M&A, strategy, competitive/market intelligence, internal audits and investor relations while working with a major IT services provider in its corporate finance team during early years in his career. At Phronesis, Ashish is responsible for expanding its world class, round-the-clock, robust & seamless delivery process and driving growth of Financial and Business Research practice across multiple clients segments including Professional services firms, Private Equity and Investment Banks amongst others. About Phronesis Partners: Phronesis Partners, one of the fastest growing research and consulting establishments globally, offers unique and actionable insights to deliver research & intelligence solutions for businesses. We take great pride in our solution-centric culture that drives client success by Simplifying Growth. At the heart of all our activities are bespoke project frameworks advanced by subject matter expertise, ensuring quality at source. A set of specialized databases, 24*7 work culture, highly qualified staff and management team weave together the right knowledge and resources to deliver business insights with direct strategic applicability. For more information, please write to info(at)phronesis-partners(dot)com


News Article | February 28, 2017
Site: www.businesswire.com

DURHAM, N.C.--(BUSINESS WIRE)--Semiconductor Research Corporation (SRC), the world's leading university-research consortium for semiconductors and related technologies, today announced that Taiwan Semiconductor Manufacturing Company, Ltd., (TSMC) has signed an agreement to participate in two SRC research initiatives. In addition to joining SRC’s New Science Team (NST) project, TSMC will be participating in the Global Research Collaboration (GRC) program. TSMC is the pioneer and global leader of the IC foundry business. The NST project, consisting of both the JUMP and nCORE programs, is a 5-year, $300M research project focused on co-optimized hardware/software solutions for high performance, energy efficient microelectronics. SRC is actively recruiting a diverse group of electronics companies to participate on the NST project that will launch on January 1, 2018. GRC is SRC’s core program consisting of eleven research thrusts that span a wide array of research topics such as analog/mixed-signal, packaging, logic and memory devices, and nano-manufacturing materials and processes. “ SRC is pleased to welcome TSMC to our research consortium of leading semiconductor and technology companies. Today’s announcement represents a strategic partnership for the research and development of disruptive technologies that extend beyond traditional scaling,” said Ken Hansen, President & CEO of SRC. “ As SRC continues to grow our global partnerships, one thing is certain, great things happen when we bring brilliant minds together! We look forward to the unique and broad perspective that TSMC can bring to SRC-sponsored research.” “ Our mission to forge a powerful innovation force in the semiconductor industry has led TSMC to this collaborative venture with SRC,” said Dr. Jack Sun, Vice President of Corporate Research and Chief Technology Officer, TSMC. “ We believe the NST and GRC research programs exemplify collaborative research amongst industry leaders that will lead to fundamental discoveries upon which TSMC will develop into leading edge process and subsystem integration solution offerings. Together, we will expand semiconductor research and development in the pursuit of next-generation innovation.” With the addition of TSMC, six of the top 10 global semiconductor companies are now members of SRC. Furthermore, this membership announcement signifies the fourth non-U.S. headquartered company to join SRC within the last 18 months. Semiconductor Research Corporation (SRC) is a world-class, non-profit consortium that works with industry, government and academia partners to define, fund and manage university research on behalf of its member companies. Through its highly regarded research programs, SRC plays an indispensable part in both research and development strategies of the most influential industry leaders. Members of SRC gain access to research results, fundamental IP, and highly experienced students to compete in the global marketplace and build the workforce of tomorrow. For more information and to stay informed on our advancements, visit www.src.org.


Choi J.-A.,Hanyang University | Kim S.H.,Corporate Research | Kim D.-W.,Hanyang University
Journal of Power Sources | Year: 2010

Ceramic-coated separators are prepared by coating the sides of a porous polyethylene membrane with nano-sized Al2O3 powder and hydrophilic poly(lithium 4-styrenesulfonate) binder. These separators exhibit an improved thermal stability at high temperatures without significant thermal shrinkage. Due to the high hydrophilicity of the polymer binder and large surface area of the small ceramic particles, the separators show good wettability in non-aqueous liquid electrolytes. By using the ceramic-coated separators, lithium-ion cells composed of a carbon anode and a LiCoO2 cathode are assembled and their cycling performance is evaluated. The cells are proven to have better capacity retention than for cells prepared with polyethylene membrane. It is expected that the ceramic-coated separator in this study can be potential candidate as a separator for rechargeable lithium-ion batteries that require thermal safety and good capacity retention. © 2009 Elsevier B.V. All rights reserved.

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