Michigan Technological University is a public research university located in Houghton, Michigan, United States. Its main campus sits on 925 acres on a bluff overlooking Portage Lake. Michigan Tech was founded in 1885 as the first post-secondary institution in the Upper Peninsula of Michigan, and was created to train mining engineers to operate the local copper mines.The university's name has changed three times to reflect expansions of degree offerings. Science, technology, forestry and business have been added to the numerous engineering disciplines, and Michigan Tech now offers more than 130 degree programs through its five colleges and schools. US News and World Report ranked Michigan Tech's undergraduate program 115th in the nation based on peer assessment, student selectivity, financial resources and other factors. Michigan Tech was also rated among the "Best in the Midwest" by The Princeton Review.Michigan Tech's athletic teams are nicknamed the Huskies and compete primarily in the NCAA Division II Great Lakes Intercollegiate Athletic Conference . The men's hockey team competes in Division I as a member of the Western Collegiate Hockey Association , and has won three national championships. The women's basketball team were national runners-up in 2011. Wikipedia.
Agency: National Science Foundation | Branch: | Program: STTR | Phase: Phase II | Award Amount: 750.00K | Year: 2016
The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase II project is the development of a portable, low cost blood typing and anemia screening device for use in blood donation centers, hospitals, humanitarian efforts and the military. This helps society because every two seconds in United States someone needs blood, yet less than 4% of Americans donate blood. Unfortunately, 15-20% of blood collected is wasted due to over collection of unneeded blood types and related blood type logistics. 12 million units of blood are collected annually in US with 108 million units worldwide. U.S. blood centers are under significant economic pressures to reduce per unit blood costs and thus waste reduction tools and strategies are in demand. Blood unit costs approach $200 per pint of blood, so this device provides the ability to pre-screen donors by blood type and selectively direct the donation process (i.e. plasma, red cells) to reduce blood product waste and better match supply with hospital demand. This portable technology could also be translated to remote geographical locations for disaster relief applications. The potential economic savings has the potential to be $400M and will contribute to reducing the overall cost of U.S. health care. The proposed project will advance knowledge across multiple fields. It adapts knowledge in microfluidics and the use of electric fields to characterize cells to identify the molecular expression on blood cells responsible for ABO-Rh blood type. This project advances the use of electric fields to rapidly measure cell concentration. This project develops software for real time tracking of cell population motion, which is highly valuable in many cell microscopy applications. This project also adapts advanced pattern recognition tools like machine learning to extract even more information from the cell behaviors. This work also extends statistical analysis from static population means to analysis of functional data - a field in its infancy - via a critical application. Finally, the device and electronics engineering will advance under the principle that "simple is best", leading to fewer potential failure points and less costly manufacture. This work advances scientific knowledge and will be published and widely disseminated after securing additional IP. It is also a powerful alternative to expensive antigen/antibody molecular recognition reactions (i.e. traditional blood typing) for medical screening and diagnosis for future point of care diagnostic applications.
Hu Y.H.,Michigan Technological University
Advanced Materials | Year: 2014
The dye-sensitized solar cell (DSSC) is representative of next generation photovoltaic devices. State-of-the-art DSSCs have been established for two decades. However, the recent application of organic-inorganic hybrid perovskites on nanoparticle Al2O3 film has totally changed the DSSC structure, leading to a new type of solar cell - meso-superstructured solar cells (MSSCs) with a high power conversion efficiency exceeding 12%. This article summarizes this impressive progress and discusses the challenges of MSSCs. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Hu Y.H.,Michigan Technological University
Angewandte Chemie - International Edition | Year: 2012
In black and white: The hydrogenation of TiO2 can extend its optical absorption into the visible and infrared region and change its color from white to black. Furthermore, the hydrogenated black TiO2 exhibits excellent photocatalytic activity for the splitting of water to yield H2. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Agency: NSF | Branch: Standard Grant | Program: | Phase: Chemistry of Life Processes | Award Amount: 420.96K | Year: 2016
Glycan-binding proteins are crucial for a wide range of biological processes. They belong to two distinct groups: lectins and glycosaminoglycan (GAG)-binding proteins (GAGBPs). A member of one group rarely possesses the characteristics of both groups. The human lectin Galectin-3 (Gal-3) is a member of the first group. This project is based on the finding that Gal-3, a lectin, actually possesses characteristics of a GAGBP. The current study investigates these newly-discovered characteristics of Gal-3. The research is integrated with educational activities through an initiative called From Bench to Blackboard. This initiative introduces glycobiology to high school students and K-12 teachers through lab- and web-based approaches. The anticipated results from the proposed work is revealing hitherto unknown properties of Gal-3 and will inspire new research activities involving Gal-3 and GAGs.
Gal-3 is one of the most extensively studied human lectins but it has never been reported as a GAGBP. However, preliminary data for this study show that Gal-3 binds to sulfated GAGs and proteoglycans. The proposed research elucidates the detailed interactions of Gal-3 with GAGs and proteoglycans. The length and sulfation level of GAGs, that are optimal for binding by Gal-3, are determined by calorimetry and spectroscopy. The GAG binding site of Gal-3 is being delineated with the use of site directed mutagenesis. Various biophysical techniques are employed to study non-covalent cross-linking of Gal-3 by GAGs and proteoglycans. In addition, competitive cross-linking of Gal-3 by GAGs and glycoproteins is also being examined. Information obtained from this research is redefining the binding properties of Gal-3 and providing a foundation for discovering Gal-3 dependent cellular and extracellular functions of GAGs and proteoglycans.
Agency: NSF | Branch: Standard Grant | Program: | Phase: RES EXP FOR TEACHERS(RET)-SITE | Award Amount: 599.59K | Year: 2016
This Research Experiences for Teachers (RET) in Engineering and Computer Science Site at Michigan Technological University (MTU) will focus on the application of computational tools for researching environmental engineering systems, an area of research and teaching emphasis at MTU. The research experiences will build on collaborative, well-established, and broad based multidisciplinary research activities at MTU. Teachers will gain an understanding of how to apply state of the art computational tools to research environmental systems and how to solve environmental problems. The tools and research projects have relevant applications in high school science (earth sciences, biology, chemistry, physics), math, and social studies. Through this RET Site teachers and their students would be provided an understanding of the discipline of environmental engineering and the research process, which is a field that requires growth and growing interest from future practitioners. This is especially societally relevant as 3 of the 14 grand challenges for engineering (National Academy of Engineering, 2015) directly relate to the field of environmental engineering.
This RET Site will offer an intensive six week summer research program for a total of 30 rural secondary STEM teachers over three years from school districts in the Upper Peninsula of Michigan, several which include significant numbers of Native American students. The proposed activities for each cohort of teachers consist of: 1) participation of RET and PhD students in a 6-week pre-research experience institute. Teachers participating via teleconference will be introduced to the goals of the RET program, provided with background on research projects, and asked to share their experience about research, inquiry-based teaching, and learning. PhD student mentors will be introduced to secondary school teaching environment and standards, inquiry-based teaching, and mentoring skills based on the Wisconsin Mentoring Seminar; 2) participation in a 6-week summer program at MTU where teachers and PhD mentors work on research projects culminating in final poster presentations; and 3) participation, during the academic year following the summer research program, in developing, implementing, and disseminating curricular materials in collaboration with PhD students and senior personnel with experience in lesson plan development. The project will achieve the following objectives: (a) secondary school teachers will: gain an understanding of the discipline of environmental engineering and the research process; learn new computational tools that they can use with their students and; gain confidence in teaching about research and science (b) secondary school students will: gain awareness of the discipline of environmental engineering; gain an understanding of how models and other computational tools are used in engineering and science and; broaden their interest in STEM careers; (c) PhD students will: deepen their understanding of research design through teaching that skill to others; gain an understanding of how to translate research to science teaching and communication and; gain experience in mentoring and team building.
Agency: NSF | Branch: Continuing grant | Program: | Phase: Macromolec/Supramolec/Nano | Award Amount: 168.79K | Year: 2017
A strategy chemists often employ in sensor design is to use a chemical called a catalyst, a substance that can bring about a chemical change. In the present project the desired change is the formation of colored products from uncolored chemicals for detection purposes. The presence of the targeted chemical can then be detected by colorimetric assay which quantifies the intensity of the colored product. While colorimetric technology is easy to use without lengthy training and with inexpensive equipment, the detection sensitivity of colorimetric assays can be less than optimum. In this project funded by the Macromolecular, Supramolecular and Nanochemistry Program of the Chemistry Division, Professor Xiaohu Xia of Michigan Technological University is studying bimetallic nanostructures as the active component of chemical sensors for properties that result from their very small particle size and their chemical composition. Their behavior mimics that of naturally-occurring peroxidases, a type of enzyme that is good at transforming biological disease markers into colored products but which suffers from low reactivity. Investigation of the synthesis and fundamental catalytic behavior of the bimetallic nanoparticles are evaluated by their use in sensitive and reliable colorimetric assays for disease biomarkers. Broader impacts of the research result from the creation of simple and affordable sensor technologies which can greatly improve our standard of living through better disease biomarker detection. Broader impacts are also made in education and outreach activities, which engage graduate and undergraduate students, especially women and minorities, in fundamental research. The research is integrated into outreach activities for K-12 students from the Upper Peninsula of Michigan. The well-defined nanocrystals, with striking shapes and uniform sizes, serve as vivid examples that are particularly appropriate for the K-12 level students to foster their interests and curiosities in modern nanoscience and nanotechnology.
In this project funded by the Macromolecular, Supramolecular and Nanochemistry Program of the Chemistry Division, Professor Xiaohu Xia of Michigan Technological University is studying bimetallic nanostructures as the active component of chemical sensors for their ability to act as peroxidase mimics toward targeted disease marker molecules. The research engineers the bimetallic nanoparticles in a unique Pd@M_nL core@shell structure, where M is composed of Pt, Ir, Rh, Ru, or Au and with the number of nanolayers (nL) equal to 1-10 atomic layers. By carefully controlling the crystallographic plane and elemental composition of the surface, Dr. Xia is able to maximize the efficiency of the bimetallic particles for improved detection limits in colorimetric assays. By coupling atomic-level electron microscopy imaging with theoretical simulations, this research addresses scientific questions of the structure-property relationships of bimetallic nanoparticle peroxidase mimics that are presently only poorly understood. Broader impacts of the research result from improved sensor technology, since as alternatives to natural peroxidase, these highly efficient mimics are straightforwardly to use for in-vitro diagnostics of disease biomarkers. Broader impacts are also made in education and outreach activities, which benefit in the engagement of graduate and undergraduate students, especially women and minorities, in fundamental research. The research is integrated into outreach activities for K-12 students from the Upper Peninsula of Michigan. The well-defined nanocrystals, with striking shapes and uniform sizes, serve as vivid examples that are particularly appropriate for the K-12 level students to foster their interests and curiosities in modern nanoscience and nanotechnology.
Agency: NSF | Branch: Continuing grant | Program: | Phase: Track 1 INFEWS | Award Amount: 1.48M | Year: 2016
PI Name: David Watkins
Proposal Number: 1639342
Title: INFEWS/T3: Reducing Household Food, Energy, and Water Consumption: A Quantitative Analysis of Interventions and Impacts of Conservation
Institution: Michigan Technological University
Changes in household-level actions in the U.S. have the potential to reduce rates of greenhouse gas (GHG) emissions and climate change by reducing consumption of food, energy and water (FEW). This project will identify potential interventions for reducing household FEW consumption, test options in participating households in two communities, and collect data to develop new environmental impact models. It will also identify household consumption behavior and cost-effective interventions to reduce FEW resource use. Research insights can be applied to increase the well-being of individuals at the household level, improve FEW resource security, reduce climate-related risks, and increase economic competitiveness of the U.S. The project will recruit, train, and graduate more than 20 students and early-career scientists from underrepresented groups. Students will be eligible to participate in exchanges to conduct interdisciplinary research with collaborators in the Netherlands, a highly industrialized nation that uses 20% less energy and water per person than the U.S.
This study uses an interdisciplinary approach to investigate methods for reducing household FEW consumption and associated direct and indirect environmental impacts, including GHG emissions and water resources depletion. The approach includes: 1) interactive role-playing activities and qualitative interviews with homeowners; 2) a survey of households to examine existing attitudes and behaviors related to FEW consumption, as well as possible approaches and barriers to reduce consumption; and 3) experimental research in residential households in two case-study communities, selected to be representative of U.S. suburban households and appropriate for comparative experiments. These studies will iteratively examine approaches for reducing household FEW consumption, test possible intervention strategies, and provide data for developing systems models to quantify impacts of household FEW resource flows and emissions. A FEW consumption-based life cycle assessment (LCA) model will be developed to provide accurate information for household decision making and design of intervention strategies. The LCA model will include the first known farm-to-fork representation of household food consumption impacts, spatially explicit inventories of food waste and water withdrawals, and a model of multi-level price responsiveness in the electricity sector. By translating FEW consumption impacts, results will identify hot spots and cost-effective household interventions for reducing ecological footprints. Applying a set of climate and technology scenarios in the LCA model will provide additional insights on potential benefits of technology adoption for informing policymaking. The environmental impact models, household consumption tracking tool, and role-playing software developed in this research will be general purpose and publicly available at the end of the project to inform future education, research and outreach activities.
Agency: NSF | Branch: Standard Grant | Program: | Phase: ENERGY,POWER,ADAPTIVE SYS | Award Amount: 500.00K | Year: 2017
As transportation and grid applications increase their dependency on batteries, challenges related to battery operation and aging dependency on the individual context circumstances remain. This is a particularly relevant problem as batteries perform multiple tasks in each application (e.g. driving, recharging, grid services, etc.) which can contribute to its aging differently. Furthermore, batteries not only perform multiple tasks in a single application, but migrate to a second application as a second life battery. This CAREER proposal aims to understand battery aging dynamics as context-dependent and to provide a unified theory and modeling that can link context events and lives with cell and module aging events. This will benefit all battery applications and the emerging battery repurposing sector by providing tangible methods to improve battery testing, estimation and management. As educational components, this project will propose new hands-on distributed laboratory capabilities for undergraduate and graduate students to explore battery technologies in the context of grid and vehicle applications. Outreach includes hosting female Hispanic students through the Michigan College and University Partnership, and also participating in the Society for Hispanic Professional Engineering mentoring and conferences.
Batteries are subjected to highly uncertain scenarios depending on their context, present cell to module and pack variations due to its space and function distribution and different monitoring capabilities at different scales. This CAREER proposal will consider that these conditions are comparable to ecological systems, such as fishery, forestry, etc. and that battery lifetime and aging should tackle the multi-scale and multi-life problem under ecological approaches and methods. For this, testing methods will include low-cost large-scale distributed testing that will experimentally probe individuals and populations of batteries across context variations and different lives. Data from these tests will be used to develop probabilistic reasoning networks to link causality for battery aging and will provide the ability to establish monitoring and data needs across lives. To formulate the battery aging and life modeling, the proposal will focus on studying intraspecific trait variations (variations inside a species) that arise from battery aging. For this purpose, populations of batteries will be identified through the establishment of a patch hierarchy to identify the structure and functional distribution of intraspecific trait per patch at the individual, sub-population and population level. The intraspecific traits will be modeled for each patch using individual-based, mixed models and integral projection models that are used in ecological systems to model population variations. These approaches will provide a probabilistic model across the population. However, as battery populations are monitored at different scales (pack and cells sparsely depending on the technology), the models will consider incomplete data availability and develop scaling ladders. These ladders will scale the intraspecific trait models from individuals to populations and vice versa to adapt to different data availability and mixed approaches. These models will be implemented in battery management systems to learn the traits models from scavenged data. Trait filters will also be formulated and deployed to identify and model internal and external factors that will determine the trait variations for each life. The models and ecology-based theory obtained will be experimentally validated through the large-scale population testing and real electric vehicle and grid-scale battery deployments.
Agency: NSF | Branch: Standard Grant | Program: | Phase: SOFTWARE & HARDWARE FOUNDATION | Award Amount: 450.00K | Year: 2016
This research is motivated by investigations on scalable methods for design simplifications of nanoscale integrated circuits (ICs). This is to be achieved by extending the associated spectral graph sparsification framework to handle Laplacian-like matrices derived from general nonlinear IC modeling and simulation problems. The results from this research may prove to be key to the development of highly scalable computer-aided design algorithms for modeling, simulation, design, optimization, as well as verification of future nanoscale ICs that can easily involve multi-billions of circuit components. The algorithms and methodologies developed will be disseminated to leading technology companies that may include semiconductor and Electronic Design Automation companies as well as social and network companies, for potential industrial deployments.
Spectral graph sparsification aims to find an ultra-sparse subgraph (a.k.a. sparsifier) such that its Laplacian can well approximate the original one in terms of its eigenvalues and eigenvectors. Since spectrally similar subgraphs can approximately preserve the distances, much faster numerical and graph-based algorithms can be developed based on these spectrally sparsified networks. A nearly-linear complexity spectral graph sparsification algorithm is to be developed based on a spectral perturbation approach. The proposed method is highly scalable and thus can be immediately leveraged for the development of nearly-linear time sparse matrix solvers and spectral graph (data) partitioning (clustering) algorithms for large real-world graph problems in general. The results of the research may also influence a broad range of computer science and engineering problems related to complex system/network modeling, numerical linear algebra, optimization, machine learning, computational fluid dynamics, transportation and social networks, etc.
Agency: NSF | Branch: Continuing grant | Program: | Phase: PHYSICAL & DYNAMIC METEOROLOGY | Award Amount: 156.12K | Year: 2017
This award allows for research on several intertwined issues related to radar meteorology, cloud physics, and the transfer of radiation through the atmosphere. The specific research tasks within the proposal will answer questions regarding why some raindrops do not fall at their terminal velocities, how radiation travels through layered dust, and how supercooled water freezes. These topics have a wide range of application, from improving radar estimates of rainfall to impacting materials sciences. The research will include international collaborators and provide educational and training opportunities for students.
The researcher will be studying the presence and impact of correlated fluctuations in cloud and precipitation physics, radar meteorology, and radiative transfer. Theoretical studies, computer simulations, and field and laboratory data analysis will be conducted to answer questions related to five main research topics: deviations of falling raindrops from their expected terminal speed, radiative transfer through a vertically stratified medium, coherence in radar backscatter using high resolution and short pulse data, temporal and spatial correlations of precipitation and their dependence on scale, and the effects of pressure fluctuations on the nucleation and freezing of supercooled water droplets.