PITA Fiscal Year 2020 Projects


Powder Bed Additive Manufacturing with Metal Powders Manufactured by Attrition
PI:  Anthony Rollett
University:  Carnegie Mellon University

In powder-based additive manufacturing, both feedstock and 3D-printing process considerably influence the final cost of the component. Production of irregularly shaped powder via metal attrition is 25-40 % cheaper and faster than gas-atomized powder with spherical morphology. A notable advantage of attrited powder is that it has no porosity by contrast to gas atomization. Although near-spherical powders are assumed to be required, success was obtained recently in previous PITA projects in demonstrating that strongly non-spherical Hydride-Dehydride Ti-6Al-4V powder can be used in both laser and electron beam powder bed fusion (LPBF). Metal Powder Works, a local startup company, will work with CMU to build on this success and demonstrate the use of irregularly shaped powder in binder jet (BJ) and LPBF technologies. Powder morphology and size distribution will be investigated for their effect on powder bed density, printability and green part density. Particular attention will be paid to the spreading step.

Autonomous Crop Disease Detection Using Spectral and Chemical Sensing and Deep Learning Neural Networks
PI:  Greg Lowry
Co-PI(s):  George Kantor
University:  Carnegie Mellon University
Agriculture is currently inefficient, unsustainable, and unable to meet future demands. A rapidly changing climate exacerbates this problem as hotter, wetter climates lead to greater crop losses from plant diseases. Sensor technology, robotics, and artificial intelligence have the potential to vastly reduce crop losses by providing autonomous early detection and treatment of diseased plants. This would significantly lower crop losses, and would lower pesticide use rates, worker exposure to pesticides, and labor costs. The proposed PITA project converges expertise in robotics, computer science, and environmental engineering at CMU, with expertise in agriculture and sensing at two PA agriculture companies to develop novel senor technologies and deep learning neural networks needed to revolutionize pest management and lower the environmental impacts of agriculture.

The specific goals of this PITA are to 1) determine which multispectral detectors can best identify affected plants before they become symptomatic, 2) determine if combinations of chemical and spectral sensors can detect diseases earlier than spectral sensors alone, 3) collect the preliminary proof of concept data needed to win a USDA SCRI and NSF Cyber-Physical Systems grant to ultimately develop fully automated robotic platforms to detect and treat crop disease before it manifests.

Containing Impacts of Communication Disruption in Energy IoT Networks
PI:  Osman Yagan
Co-PI(s):  Javad Mohammadi
University:  Carnegie Mellon University

The objective of this project is to study the impact of communication disruptions on coordination and operation of Distributed Energy Resource commonly referred to as DERs (such as smart thermostats and storage systems). Majority of these DER are connected to communication networks and can be considered as Internet of Thing (IoT) devices. We aim at mitigating the aggregate impact of widespread communication disruptions on functionalities of IoT-connected DERs and power system operations. Understanding the dependencies in these complex cyberphysical systems (IoT-connected DERs and communication networks) and mitigating the potential risks are critical for the successful development and evolution of smart infrastructure systems. Affordable energy resources are shaping the future of power networks and their resilient and secure operation is of prominent importance for maintaining reliability of electric power network. This project aims to advance the state-of-the-art in modeling interdependence and analyzing and improving robustness through an inter-disciplinary approach combining theoretical analysis, empirical modeling, and optimization. Study on the vulnerability and robustness of interdependent systems has great implications for planning and emergency management of electric power network. To this end, we will leverage our campus wide energy IoT test-bed to model a network of IoT-connected DERs as virtual and physical agents in a multi-agent environment. Then, we will study impact of communication disruption on IoT functionalities and power grid operation. We will also develop algorithms and technology to contain communication disruption across IoT networks. In order to further increase robustness, we will propose measures to increase resiliency of the IoT network. We will collaborate with our industry partner (Grid Fruit LLC) to test the performance of proposed methods using their aggregation platform.

Scalable Assembly of Quantum Dot Hybrid Materials for Luminescent Panels
PI:  Michael Bockstaller
Co-PI(s):  Krzysztof Matyjaszewski
University:  Carnegie Mellon University

Semiconductor quantum dot (QD)-based full color luminescent panels are expected to provide breakthrough advances in the area of energy efficient active display and lighting technologies. To facilitate this type of application established manufacturing processes apply four-color screen or inkjet printing processes to fabricate monochromatic zones of distinct-color QDs. The sequential nature of multistep zone printing presents a formidable challenge for the commercialization of QD-based active display and lighting technologies because it limits scale-up and cost efficient production. The overarching technical objective of this proposal is to develop a transformative new bottom-up approach for the high throughput and cost efficient production QD-based luminescent panels by harnessing the autonomous organization of mixed polymer-modified QD systems into monochromatic domain structures.

The scientific objectives of this project are threefold: First, to adopt a recently developed initiator variant to the synthesis of poly(methyl methacrylate), polystyrene and poly(styrene-r-acrylonitrile) tethered QDs. Second, to evaluate the effect of polymer modification on the luminescence efficacy of QDs, and in particular to identify conditions for maintaining high luminescence efficacy in red QD systems. The third objective of the proposed program is to demonstrate the possibility of the autonomous organization of mixed QD films into monochromatic domain structures. The results of the proposed research will provide a foundation for the development of new and transformative fabrication strategies for the cost-efficient production of full-color quantum dot-based luminescent panels by replacing the established fabrication process based on multistep zone printing with a uniform coating process and the subsequent self-assembly of quantum dots in monochromatic domain structures.

Enhancing Cementitious Materials Thermal Performance with Phase Change Materials for High Temperature Thermal Energy Storage 
PI: Sudhakar Neti
Co-PI(s): Clay Naito, Nasser Vahedi
University: Lehigh University

Thermal Energy Storage (TES) offers a critical solution to compensate for the intermittent nature of concentrating solar energy.  TES can also contribute to improving the flexibility of existing fossil power plants, which are currently faced with cycling dispatching, due to the penetration of renewable power. Concrete and cementitious materials are promising candidates for sensible heat thermal energy storage at large scale and high storage temperatures. One of the drawbacks of concrete is its inherent low energy storage density which results in more required storage material, especially in large bulk storage systems. Integration of phase change material (PCM) within concrete is an innovative solution to address this shortcoming and make the storage system more efficient and compact. At Lehigh University, a team of faculty, researchers and students are engaged in state-of-the-art research in the area of TES.  This includes research in sensible, latent and thermochemical energy storage.  The group aims at developing a center for high-temperature TES at Lehigh.  As part of these activities, members of the Energy Research Center (ERC) have joined efforts with faculty from the Advanced Technology Large Structural Systems (ATLSS) Center in a large project that aims at developing an integrated concrete-thermosyphons solution for the application of coal-fired power plants.  This project is funded by the US Department of Energy (DOE).  The scope of work in this project involves engineering cementitious materials to improve its thermal characteristics and interfacing them with two-phase heat transfer devices, embedded in the concrete matrix.  This proposed project is for a concept in which PCM is directly embedded into the concrete matrix, making it an integrated solution for TES.  The proposed research will include screening potential PCMs for high temperature applications (300-600 deg. C), while considering their compatibility with concrete, material selection/preparation, integration method evaluation, such as micro or macro encapsulation, graphite foam infusion or pellet insertion. The scope of work will also include an analysis of PCM integration on concrete physical properties, and a study of cyclic performance and thermal stability of the integrated solution under multiple storage cycles. The topic and project objectives are in line with Pennsylvania Infrastructure Technology Alliance's (PITA's) goals and objectives; additionally, the project will enhance the technical capabilities of Lehigh University in this topic area. This area of research has great potential for commercial applications in the solar industry and power generation plants. The proposed project will include cost share from the current DOE grant using concrete for sensible storage at large scale. As it will be set-up, the results of the proposed study would be available for direct application in that DOE project.

Coal Fired Power Plant Slipstream Evaluation of Activated Anthracite for Mercury Emission Control
PI: Carlos Romero
Co-PI(s): Zheng Yao
University: Lehigh University

Activated carbon is a sorbent material widely used for removal of harmful pollutants and impurities in gases and liquids.  One application that has gained increased attention is the application of activated carbon for mercury removal from coal-fired power plant flue gas.  Mercury is a toxic metal targeted for emissions control, due to its toxicity, high volatility and potential for bioaccumulation.  The market for activated carbon for mercury emissions compliance is large and active.  Activated carbon can be prepared from different raw materials that include coal (primarily bituminous and lignite) and biomass.  With the recent decline in coal markets, anthracite coal producers have been exploring alternative market opportunities for their coal production.  Blaschak Coal Corporation mines Pennsylvania anthracite coal and is actively searching avenues and new markets for its products.  Blaschak has partnered on a project, funded by Ben Franklin Technology and Partners, with Lehigh University and the University of Kentucky that aimed at characterizing the full range of anthracite sources it mines at various mine sites and from multiple coal veins.  A review of anthracites have indicated the potential of this coal rank to be an efficient sorbent material, comparable to commercially activated carbon from other raw materials.  Blaschak desires to use these characterization results as the basis for selection of raw anthracite to be used for activated carbon preparation.   Blaschak also participated with Lehigh in a laboratory feasibility study funded by Pennsylvania Infrastructure Technology Alliance (PITA).  This project proved the merit of anthracite to produce activated carbon. Lehigh University and Blaschak intend to carry out a second phase project under PITA’s funding to investigate the feasibility and performance, in the field, of activated anthracite for mercury capture in a coal fired power plant flue gas silpstream, in a way that is competitive with commercially available products.  Testing in a power plant is provide a change to see the field performance of the activated material since the real flue gas stream contains multiple compounds that would impact the actual performance of the product.  Results from this study will be presented in terms of the sorption characteristics and mercury capture efficiency of the activated anthracite-based carbon in comparison to a benchmark product commonly used by coal-fired power plants.  A report will be prepared summarizing the methods, equipment and procedures used in the field test, as well as the test results and a discussion on the assessment and potential of a larger scale test project of activated anthracite for mercury capture applications in flue gas at a power plant.

Atomistics of Silver Diffusion in Pastes for Screen-Printed Silicon Solar Cells
PI: Ganesh Balasubramian
University: Lehigh University

Research and development of novel, low cost, environmentally-friendly glass frits used in silver pastes during screen-printing of silicon solar cells is being proposed. Though crystalline silicon technology is well-known in solar industry, improved power conversion efficiency with substantially lower cost is needed for wider adoption across all infrastructures. In this project, we aim to understand the diffusion behavior of silver particles in glass frits as a function of (1) glass composition and (2) fabrication conditions. A transformative approach to design the silver paste and processing frit will provide a cost-effective, superior conductivity and chemical stability of the electrodes in the solar cells. To accomplish this, we will pursue a computational materials engineering guided approach of effectively predicting the variation in diffusion coefficient as a function of possible frit compositions and manufacturing conditions. The computational design will be improved with feedback from materials characterization and measurements of silver-silicon interface structure and properties. Through this industry-university partnership and with leverage support from the industry and NSF, the graduate student will be able to participate in cutting-edge renewable energy technology, contribute to fundamental research in materials development and potentially engage in product development and commercialization in collaboration with a PA company.

Study of Controlled De-activation of Smart Release Layer for Polyethylene Waterproof Membranes
PI: Daniel Ou-Yang
Co-PI(s): Lanfang Li
University: Lehigh University

High Density PolyEthylene based Water-Proofing Membrane (PEWPM) is a high performance waterproofing material designed for infrastructure construction such as transportation tunnels, basement, and other underground facilities.  The membrane comprises of extruded HDPE, a Pressure Sensitive Adhesive (PSA) layer and a protective coating and a release film liner.  The long term waterproofing function derives from the HDPE membrane and a sacrificial protective layer is coated on PSA to protect the PSA layer from sticking to unwanted substrate or dirt until installation. This sacrificial release layer (SRL) needs to be a smart layer in that it is active and providing protective benefit during material storage and transportation, but automatically deactivates upon deployment.  It is desirable to improve on the SRL which can have substantial impact on pollution, waste generation and hazard mitigation during manufacturing.  A water based coating system is being developed by Oriental Yuhong North American LLC and this proposal is designed for a collaboration with Lehigh University to better elucidate the structure property relationship of the coating to achieve the required property balance of the protective function.

Technology and Economic Assessment of Increasing Solar Penetration in PPL Service Territory
PI: Rudy Shankar
University: Lehigh University

Solar energy is the fastest growing means of clean energy. A full assessment on future solar penetration is pertinent as the Pennsylvania state legislature considers HB 531-Community Solar Projects. This effort would provide a comprehensive overview of the Bill impact as well as recommend optimal strategies going forward.

Machine Learning Dynamical Models of Chemical Processes from Temporal Data
PI: Srinivas Rangarajan
Co-PI(s): Mayuresh Kothare
University: Lehigh University

Most chemical and biological processes are dynamical systems. This means that their state variables (i.e. variables  that together characterize what state the system is in) are continuously changing, often underlined by highly nonlinear correlated behavior that many not be easily captured by physics-based models. Modern plants in the energy and chemical industry have advanced data acquisition technologies, enabled in many cases by solutions offered by OSISoft LLC, the industrial participants of this project. These technologies allow for collecting, storing, and analyzing data from thousands of sensors every second (or faster). Our ultimate goal in this context is to leverage such data to design, optimize, and control new energy and chemical systems, i.e. in the absence of accurate first-principles models. In this project, we will begin addressing this larger goal by developing algorithms that will allow us to extract the underlying ordinary differential equations from time-varying data. Such an algorithm will then allow us to take time-varying plant data and build data-driven dynamic equations that accurately captures the overall process. We specifically intend to build on the state-of-the-art algorithms from the applied mathematics community on inferring equations from data that have been successfully applied in the fluid mechanics domain by incorporating a number of new features including the concept of infusing chemical engineering domain knowledge as constraints while training the data-driven model. This project will focus on developing this method and testing it on industrial datasets with software supplied by OSISoft.


Assessing Reliability-Based Demand Flexibility of Commercial Buildings
PI:  Mario Berges
Co-PI(s):  Burcu Akinci
University:  Carnegie Mellon University

Traditional demand response (DR) has played an important role in the power sector for decades. With an increasing share of variable renewable electricity generation, managing the grid becomes a more challenging task. In recent years, PJM, the interconnection that serves the Commonwealth of Pennsylvania, has studied the possibility of demand side resources becoming part of the grid-interactive assets that enable this needed operational flexibility. Buildings are particularly well suited to address this due to increasing levels of automation as well as their thermal flexibility allowing for the systems to be loaded and unloaded without affecting their performance. However, these reliability-based forms of fast DR require novel approaches in automation and increasing levels of transparency of response that were not required in traditional DR. Therefore, in this project, we are partnering with both Bosch Research and Technology Center and CMU’s Pittsburgh campus Facilities Management Services (FMS) to leverage a wide range of aptly equipped testbeds and expertise to evaluate the performance of commercial buildings participating in ancillary services.  In the topic of demand side flexibility, occupancy and thermal comfort play a major role. Therefore, our partnership with Bosch greatly enhances this project by allowing access to vast datasets of occupancy information as well as a non-academic testbed in their Pittsburgh office. In addition, given expressed interest from the FMS team to explore the participation in fast DR, we will build a relationship with the DR program provider to understand the requirements of these services. As an outcome, we can deliver FMS with an independent evaluation of these DR events. It is our expectation that the products of this project create transparent and reliable demonstrations of buildings providing these ancillary services based on scrutiny of the data we obtain.


Engineering Educational Outreach
PI:  Megan Fahey
University:  Carnegie Mellon University

The College of Engineering's educational outreach programs share the university's resources with the local community, integrating Carnegie Mellon students, faculty, and staff with K-12 students. These programs – including but not limited to Moving 4th Into Engineering, the Summer Engineering Experience for Girls (SEE), and Engineering @ CMU – provide a breadth of free opportunities for local students to gain hands-on experience and knowledge about education and careers in math, science, and engineering. Moving 4th Into Engineering partners with local fourth-grade teachers to recruit students they feel will benefit from a day of hands-on engineering activities on campus. The SEE program provides a summer camp in which area middle school girls engage in engineering activities led by Carnegie Mellon students, faculty and staff. For Engineering @ CMU, the College of Engineering hosts one of the site program for the Allegheny Intermediate Unit’s Apprenticeship Program for high school students in the county. Finally, funds are used to develop new age-appropriate, standards-grounded demos and activities for K-12 students.

CIAMTIS UTC 2020 Research Experience for Undergraduates (REU) Program
PI: Richard Sause
University: Lehigh University

Lehigh University, through the Institute for Cyber Physical Infrastructure and Energy (I-CPIE) and the Advanced Technology for Large Structural Systems (ATLSS) Engineering Research Center, is proposing a 10-week Research Experience for Undergraduates (REU) program as part of its University Transportation Center (UTC) collaboration with Pennsylvania State University.  The REU program, entitled the Center for Integrated Asset Management for Multi-Modal Transportation Infrastructure Systems (CIAMTIS) REU program, will fund 6 students (3 students funded through PITA and 3 students funded through CIAMTIS), targeted from a CIAMTIS member university.   The program’s focus will include the assignment of the REU student to an active Lehigh University CIAMTIS research project under the direction of the project Principal Investigator and graduate student mentor, in order to help the student navigate through the research experience in the transportation area. The research project will be a Year 1 or Year 2 funded project through CIATMIS.  Beyond the research experience, the program will expose the students to a well-rounded professional development experience.  The program’s activities will include professional skills development workshops and seminars, onsite outreach activities, and offsite tours.  The program will culminate with a final report, poster, and presentation on the research findings and presentation of the poster at the 2020 CIAMTIS Transportation Asset and Infrastructure Management (TAIM) conference.


Novel High-Density Electrode Platform for Tumor-Treating-Field Treatment of GBM
PI:  Marc Dandin
Co-PI(s):  Pulkit Grover; Siyang Zheng
University:  Carnegie Mellon University

In this proposal, we set forth a research and development plan for optimizing tumor treating field (TTF)-mediated chemotherapies for glioblastoma multiforme (GBM), commonly known as brain cancer. We will use a murine brain-slice GBM model integrated in a custom-designed integrated microsystem for monitoring TTF and chemotherapy effects on the tumor and its microenvironment within the brain slice. Our end goal is to provide a patient-centric precision medicine solution for tailoring TTF spatiotemporal parameters and chemotherapeutic regimens that yield patient-optimized treatments, while accounting for TTF effects on both cancer and healthy cells. With our approach, we expect to create TTF stimulation regimens that tailor field directionality, gradient, intensity, and temporal waveform, to chemotherapy potency and to specific tumor characteristics. Furthermore, with our platform, a clinician will be able to screen chemotherapeutic drugs in combination with TTFs against biopsied primary tumor cells to create an optimum patient-specific treatment regimen. Our platform can be integrated directly in the current standard of care, in order to provide improved treatment outcomes, thereby increasing the cost-effectiveness of TTF-mediated chemotherapies.

Scalable production of intrinsically chiral surfaces for enantiospecific applications
PI:  Andrew Gellman
Co-PI(s):  Nisha Shukla
University:  Carnegie Mellon University

Control of molecular chirality (handedness) is critically important in the field of pharmaceuticals production. It is also one of the most challenging forms of chemical synthesis because the two enantiomers of a chiral pharmaceutical have identical properties in achiral environments.  Surfaces with chiral structures at the atomic level are often used as chiral environments for production of enantiomerically pure chiral products.  Most often, the chirality of such surfaces derives from adsorbed monolayers of chiral organic molecules.  I discovered the existence of intrinsically chiral metal surfaces and my group has pioneered the study and understanding of their enantiospecific chemistry. [1] Chiral surfaces are the high Miller index planes of crystalline metals with no bulk mirror planes perpendicular to the exposed surfaces.  These have been shown to exhibit extremely high enantiospecificities in various surface reactions. [2] One of the interesting challenges that now faces the ultimate exploitation of chiral metal surfaces is the need for their production by scalable, cost effective means.  A number of possible methods for manufacturing chiral metal surfaces in large surface area formats have been discussed recently. [3]  This proposal requests funds to demonstrate a new approach that is based on the use of Rolling Assisted Biaxially Textured Substrates (RABiTS) which have been developed as substrates for growth of high Tc superconductors.[4,5]  In essence, RABiTS are rolled metal foils that are giant single crystals.  The work proposed will serve as the proof of concept demonstration that the surfaces of RABiTS can be nanotextured to yield naturally chiral surface orientations in large area formats.  These will be shown to have enantiospecific interactions with chiral adsorbates.  This proof-of-concept demonstration will serve as the basis for federal funding to establish a new research direction.

Soy protein based wound dressings-evaluation for cell biodegradation and delivery of therapeutics
PI:  Phil Campbell
University:  Carnegie Mellon University

Soy proteins, along with other plant-based proteins, have recently been explored as a "green" renewable source material for biomedical applications. As a non-animal source material, there is no disease transmission concerns as are associated with human and animal sourced materials. Soy protein isolate (SPI) represents for all practical concerns an inexhaustible source material with excellent storage stability across a broad range including 23-40C, thus requiring no special storage conditions. Soy proteins can mimic extracellular proteins promoting cell attachment and proliferation. Furthermore, in cutaneous wound healing, SPI based materials improve wound healing, and appear to be biodegradable, allowing for subsequent applications directly onto the prior applied materials. However, the inclusion of therapeutics, such as growth factors and extracellular vesicles (EVs), with soy protein-based biomaterials to further enhance healing has not been considered to date. And, the addition of such therapeutics may likely be required to overcome the compromised wound environment such in diabetic patients. Furthermore, biodegradation of soy proteins has thus far been based on orally delivered protein or upon physical observed changes in soy protein biomaterials in vivo over time. In this project, we will evaluate of SPI-based materials for binding, retention and bioactivity of growth factors and EVs in vitro, and evaluate cell-based trafficking and proteolytic degradation of SPI-based materials. The resulting data these studies will enhance the scientific basis and marketing profile of our industrial partner, NeuEsse, a Pennsylvania based startup, developing OmegaSkinTM for dermal wound applications. Furthermore, resulting data will form the nexus for future applications, including NIH SBIR/STTR and DOD, for further development including preclinical animal through to clinical studies.

Chemo-mechanical Interlocking for Ingestible Oral Drug Delivery Devices
PI:  Christopher Bettinger
University:  Carnegie Mellon University

Compliance with oral medications is poor, costing the US healthcare system $100BB/yr annually and contributing to ~100,000 premature deaths each year. Compliance with oral medications varies inversely with the frequency of administration. Reducing the frequency of administration by increasing the residence time of ingested drug delivery systems can dramatically improve compliance. However, current strategies to increase the residence time of ingestible devices are largely ineffective.

This project will leverage in-house expertise in biodegradable elastomers, polymer processing, and genipin-eluting systems to manufacture device-based oral delivery systems to increase the residence time within the small intestine of the GI tract by up to 10X. Specifically, we propose a centimeter-scale flexible self-expanding mucoadhesive device that resists peristalsis by mechanical interlocking and increasing mucin viscosity. The device will feature: (1) micropost arrays that mechanically interlock with the villi of the small intestine and (2) reservoirs to elute genpin, a natural protein crosslinker, to chemically stabilize local mucin networks and increase the viscosity. Chemo-mechanical interlocking increases mucoadhesion at the tissue-device interface, resist peristalsis, and therefore increases the residence time for oral drug delivery devices transiting the GI tract.

Remote Health Monitoring via 3D Printed Wearable Electronic Decals
PI:  Rahul Panat
Co-PI(s):  Gary Fedder
University:  Carnegie Mellon University

Remote monitoring of patient vital parameters is revolutionizing healthcare by improving patient quality of life and reducing healthcare costs. Current state-of-the-art devices that monitor biomarkers, however, are bulky and uncomfortable to use over long periods of time due to limitations in their manufacturing technology. The PIs propose to collaborate with Allegheny Health Network (AHN), a PA-based healthcare provider, to develop a 3D printing process for compact, wearable, and flexible electronic decals (a.k.a. tattoos) for in-situ monitoring of human biomarkers. The PIs will use aerosol jet 3D printing technique to connect and integrate multiple sensors on a soft substrate that is comfortable to the skin for long-term use. The current research builds upon an existing collaboration between AHN and the PIs, and will focus on the design and fabrication of decals for electrophysiological sensing and motion sensing. The decals will be tested for mechanical and electrical stability under a high strain. Note that the PIs have a strong complementary expertise in the fields of advanced manufacturing (Panat) and device design and testing (Fedder). The proposed research will foster collaboration between industry and academia within Pennsylvania, improve public health and healthcare infrastructure, train the next generation of engineers from the commonwealth, and seed research that attracts funding from federal agencies. The PITA grant will help PIs obtain the proof-of-concept data which will be used to apply for larger grants with Health and Human Services (HHS), National Institutes of Health’s (NIH) R01 program, and National Science Foundation’s (NSF) LEAP-HI program by the end of the project period.

Develop Extracellular Vesicle-like Metal–organic Framework Nanoparticles for Intracellular Delivery of Enzymes for Inherited Metabolic Disorders
PI:  Siyang Zheng
University:  Carnegie Mellon University

Intracellular enzymes deficiencies contribute significantly to the child morbidity and mortality in inherited metabolic disorders (IMD). It is projected that at least 28 000 children will be born with IMD every year globally. However, current enzyme replace therapy (ERT) is based on the delivery of native enzymes into the circulation and has many drawbacks, which limits its wide adoption. To meet this urgent and unmet clinical challenge, in this proposal, we propose to develop an extracellular vesicle (EV)-metal organic framework (MOF)-protein (EMP) nanoparticles platform for intracellular enzyme delivery for IMD treatment. The EMP nanoparticle leverages the fundamental characteristics of biomimetic MOF for caging guest proteins/enzymes with high efficiency and loading capacity. Furthermore, EMP nanoparticle leverages the fundamental structural characteristic of EVs, their lipid membrane, to enable systemic targeted enzyme delivery with significantly longer circulation time. In our preliminary study, we have demonstrated that guest proteins are caged in the matrix of MOFs with high efficiency (up to ∼94%) and high loading capacity, and the nanoparticles are further enveloped with the EV membrane with high efficiency of ∼97%. Importantly, assisted by EMP nanoparticles, intracellular delivery of the therapeutic protein gelonin significantly inhibits tumor growth in vivo and increased therapeutic efficacy by 14-fold. Herein, we propose to develop the EMP nanoparticles with MOF loaded with alanine glyoxylate aminotransferase (AGT) and coated with cells-derived EV membranes for targeted intracellular enzyme delivery of AGT into the liver cells for a proof-of-concept study of hyperoxaluria type 1 (PH 1) treatment. Aim 1 will develop a highly efficient EV generation method. Aim 2 will synthesize EMP nanoparticles caged with AGT with high efficiency and high loading capacity.  Aim 3 will characterize EMP for intracellular AGT delivery to HepG2 cells with AGXT gene knockout. Future studies will focus on the in vivo characterization of EMP nanoparticles with AGT.

Development of a Safe Chelator of Lead to Enable the Treatment of Low-level Lead Poisoning
PI:  Stefanie Sydlik
University:  Carnegie Mellon University

No safe level of lead in the body exists, according to the CDC; yet, lead poisoning remains a significant public health concern. While policy efforts, such as removal of lead from gasoline and paint, have decreased average blood concentrations, substantial portions of the population, many of them children, still possess lower but nonetheless dangerous blood lead levels (< 5 mcg/dL). The status quo treatment for these individuals is environmental remediation of lead; however, remediation may not remove all sources of lead. Further, lead is biopersistent, with blood, soft tissue, and bone half-lives of weeks, months and decades. Thus, removing lead from the body would be highly desirable. Chelators are molecules that bind metals and are used in chelation therapy to treat metal poisoning. Unfortunately, chelators are also associated with serious side effects, limiting chelation therapy to only those patients with acutely high blood concentrations of lead (> 45 mcg/dL) where the risks of chelator side effects are mitigated by extreme lead toxicity. Therefore, there is a significant need to create biocompatible chelators that can safely treat patients with low blood levels of lead. The goal of this proposal is to functionalize safe biomolecules with a known lead chelator. The biomolecule component will mitigate the toxicity of the chelator, and its structure can be tuned for oral or intravenous administration. Ultimately, the new “biochelators” should enable the safe chelation of low levels of lead in the blood, thus preventing chronic toxicological effects of lead.

Quality Investigation of Bone Marrow Aspirate Concentrate
PI: Sabrina Jedlicka
Co-PI(s): Susan Perry
University: Lehigh University

One in four adults in the USA have a form of arthritis, costing $81 billion/year for medical interventions.  Treatments for arthritic disorders, such as osteoarthritis, range from over-the-counter medications to surgery.  Recently, the use of minimally processed bone marrow-derived products, clinically known as “Bone Marrow Aspirate Concentrate” (BMAC) has become common.  Bone marrow is extracted from the patient, rapidly processed using clinically available tools, and the concentrate is injected back into a patient joint.  This BMAC material comprises of white blood cells, red blood cells, mesenchymal stem cells, proteins, platelets, and other biological/biochemical materials.  Different concentration devices yield variable compositions of BMAC. Regardless of the device used, ~65% patients often experience rapid (<2 weeks) and dramatic, albeit palliative relief with these treatments.    However, the variability in BMAC composition is a cause for concern, and is likely the driver of variable patient responses.  Original patient specimen quality is inconsistent (viscosity, cell counts, etc.), and a robust biomarker of “quality” BMAC has been elusive.  Previous work indicates that there is likely an overlooked biochemical or cellular cross-talk element, which will be explored in the proposed research.  As FDA regulations evolve to meet the changing clinical landscape, standardization of BMAC protocols are imperative to allow for continued access to a life-changing treatment.  Thus, the proposed partnership will focus on two unexplored facets of clinical BMAC science:  (1) Comparative cell profiles of BMAC processed through different concentration platforms and (2) Examination of how BMAC biochemical payload is altered during concentration, in the form of extracellular signaling particles. The long-term goal is to develop a robust separation device that allows for product consistency and enhanced biomolecular concentration.  Working collaboratively with a local orthopedic surgeon, we hope to develop a methodology and translational technology that will align with the emerging FDA goals related to BMAC protocols.

The Study of How Multi-Drug Resistant Bacteria are Spread Throughout Water Systems by Free Living Amoeba
PI: Kristen Jellison
Co-PIs: Sabrina Jedlicka
University: Lehigh University

Free-living amoebae (FLA) are ubiquitous protozoa found in the environment that can survive under harsh environmental conditions, such as those typically found in water treatment facilities. Certain bacteria can survive, and even grow, inside FLA; one such bacteria of concern is Stenotrophomonas maltophilia, an environmental pathogen that has been recovered from soil, rivers, lakes, and plant roots, as well as bottled water and sink drains. It is a multi-drug resistant bacteria that has been associated with infections in immunocompromised people, especially in hospitals.

The goal of this project is to study and identify the species of FLA in local water treatment networks in the Lehigh Valley, as well as to determine if, and to what extent, S. maltophilia is harbored by those FLA. Water samples will be collected at different stages of the water treatment process to understand how effectively the most common treatment technologies remove or inactivate the FLA and S. maltophilia. Samples will also be collected from locations throughout the water distribution network, including at hospitals, medical facilities, and private residences, as well as finished water holding tanks.

Once the most common FLA in our local water treatment networks are identified, we will begin laboratory testing to study how these FLA enhance the growth of S. maltophilia as well as how the FLA trophozoites and cysts protect S. maltophilia from hostile environmental conditions and water treatment technologies.


AI Training Data Improvements and Explainability
PI:  Asim Smailagic
Co-PI(s):  Dan Siewiorek
University:  Carnegie Mellon University

Explainable detection research in machine learning is a new and important are. We propose our method for the task of detecting Diabetic Retinopathy (DR) lesions from a dataset of eye fundus images containing a single scalar label for the whole image. DR is a worldwide leading cause of preventable blindness affecting more than 25% of the estimated 425 million diabetic patients in the world. Trained via weak supervision, our model pinpoints regions of the image containing lesions indicative of DR, thereby providing a highly valuable explanation when the model predicts presence of disease. Our method can convert any pre-trained convolutional neural network into a weakly-supervised model leading toward results showing the converted model provides both increased performance and efficiency. Next, we introduce a novel Online Active Deep Learning method for Image Analysis with a sampling method that queries the unlabeled examples maximizing the average distance to all training set examples. We will also experiment how domain knowledge that is encoded into a deep neural network by pre-training can improve the performance and explainability of a classification task. We will be producing attention maps at intermediate steps during the training process as to improve explainability of a task.

Holistic Detection of Human Trafficking in Online Escort Advertisements
PI:  Christos Faloutsos
University:  Carnegie Mellon University

Given on-line escort advertisements, how can we quickly spot the ones that are near-duplicates, and thus are suspicious for organized, human trafficking?  How can we quickly summarize our findings, so that law enforcement can easily decide which leads are promising, and which ones are not?  We propose 'TrafficLight', a system to handle both issues.  For the first problem, we will use advanced clustering algorithms based on the so-called 'singular value decomposition', to automatically group near-duplicates.  For the second part, we propose to summarize and highlight the similarities, to make it easier for inspection and verification.  The CMU team will continue its collaboration with Marinus Analytics, a PA, female-owned company that does pioneering work in human trafficking detection.

Improved Cybersecurity for Cyber Physical Sensor Systems Performing Classification
PI: Rick Blum
University: Lehigh University

This research project will develop detection and mitigation techniques for cyber-attacks on systems trying to classify objects based on sensor observations.  The Pennsylvania company partner I2R Electronics Inc., part of Nanowave Technologies Inc., is making such products and our goal is to develop technology which will ultimately protect their products from cyber-attacks. The project will build on research results from previous work at Lehigh University for systems trying to estimate parameters, like object position.  The team intends to devise provably effective algorithms for identifying attacked data as well as employing the unattacked and attacked data in provably near optimum, low complexity classification approaches. Similar to the previous Lehigh research, the team will develop analytical formulations of the best possible classification approaches given either attacks or no attacks.  These results will be used to describe the best possible processing under attack, but these approaches will require high complexity.  Performance bounds can be generated by assuming the attacked system knows exactly which data is attacked.  The team will search for low complexity algorithms that can provide performance close to these bounds.  Low complexity unsupervised machine learning approaches will be studied that mimic the developed high complexity analytical formulations of optimum classification under attack based on the previous Lehigh research.


Modeling and Enhancing Freight Mobility in the Philadelphia Region
PI:  Sean Qian
University:  Carnegie Mellon University

The Philadelphia region has a large and complex freight transportation network that includes more than 1,000 miles of the National Highway System and 9.8 million vehicle-miles of daily truck travel. The mobility of commercial trucks and the efficiency of freight infrastructure are essential to regional transportation infrastructure planning and economic development. Unfortunately, characteristics of freight demand, such as when and how trucks travel, freight destinations and truck routing behavior, are unclear. This becomes the main hurdle for improving truck mobility. There lacks of freight models that predict the mobility of truck demand induced by ‘what-if scenarios’, such as roadway construction, new freight terminals and land-use change. This research will analyze the freight movements from the intermodal facilities in the Delaware Valley Regional Planning Commission (DVRPC) region, including ports, airports, and rail terminals, to understand travel destinations, travel routes, touring behaviors, time of day of travel and other travel patterns of the freight truck movements generated from these facilities, using the truck GPS data purchased and provided by DVRPC. By incorporating the characteristics of truck demand, this research will also develop a regional network model that encapsulates respective route choices of cars and trucks, the mixture effect of flow of cars and trucks, and estimate/predict high-granular car and truck network flow. As a proof-of-concept experiment, we use this network model to forecast the traffic conditions of trucks induced by the closure of an I-95 highway segment between Ben Franklin Bridge and Broad St. This model and tool will be provided to DVRPC for their decision making on freight planning and operation.

Risk- and Sustainability-informed Decision-making for Durability Enhancement and Service Life Extension of Steel Bridges Using Maintenance-free Steel           
PI: Dan Frangopol
Co-PI(s): David Yang
University: Lehigh University

Aging infrastructure poses substantial threat to the prosperity and sustainability of society. Bridges, as critical nodes in the transportation infrastructure, are especially crucial. In Pennsylvania, the average age of bridges recorded in the National Bridge Inventory is over 40 years ago. Many of these bridges have been servicing beyond their initial design service life. Still, these overage bridges serve millions of users every day, posing considerable risk to the functionality of the transportation infrastructure and to the safety of traffic users. Nevertheless, a total overhaul and rebuilding of all these bridges are neither realistic from a budgetary standpoint nor reasonable given that over-design is common in bridges. Therefore, sensible end-of-life (EOL) management of aging bridges, including the planning and implementation of in-depth inspection, load rating/posting, bridge rehabilitation, and, in severe cases, bridge demolition and rebuilding, is urgently needed to ensure that the risk of bridge failure be under an acceptable level. To this end, the main objective of this project is to establish a risk- and sustainability-informed EOL decision-making framework for aging bridges in order to extend their useful service life. This objective will be achieved through (a) risk- and sustainability-informed intervention prioritization and (b) value-based EOL management that delivers high return on infrastructure investment (ROI) and long extended service life. Emphasis will be put on simply supported non-composite steel (SSNCS) bridges considering their prevalence in PA and their relative old age compared to other bridge types. For rehabilitation planning, this project will emphasize the use of maintenance-free steel, a locally sourced material in PA, due to its high durability performance and potentially significant cost-effectiveness from a life-cycle perspective. Ultimately, the project will benefit the Commonwealth and the Nation in solving the Grand Challenge set out by ASCE – “reducing the life-cycle costs of infrastructure by 50 percent by 2025”.


A Novel Bio-Nano Process for Energy Efficient Inland Wastewater Recovery
PI: Arup Sengupta
Co-PI(s): Derick Brown
University: Lehigh University

In cities and metropolis around the world, municipal wastewater after secondary treatment is being increasingly viewed as a large and resilient water resource that is insulated from climate change effects. In Orange County Water District (OCWD) in Los Angeles and San Diego in California, large treatment processes are in place to transform waste water into usable water. Total dissolved solids (TDS), a measure of salinity, normally varies between 500-1000 mg/L in municipal wastewaters globally. Thus, high energy-consuming reverse osmosis (RO) to achieve TDS reduction is the most significant treatment step for every secondary municipal wastewater recovery plant tried to date globally. For 150 million gallons per day (MGD) plant in OCWD in Los Angeles, energy consumption is 1.2-1.5 kWh for 1000 liters of treated water and 90% of it is due to RO. Also, nitrate and phosphate present in the RO reject are routinely discharged into the ocean.

We have developed a carbon dioxide (CO2) driven bio-nano wastewater treatment process that i) transforms nitrate in nitrogen; ii) recovers phosphate as a high purity fertilizer; and iii) reduces salinity without needing electrical or mechanical energy. Carbon dioxide at 10 atmosphere is used as the sole source of energy and the process produces no major disposable waste. In this project, we will use treated wastewater from the Allentown plant and carry out the bio-nano process in our laboratory to demonstrate the feasibility of the process. We already submitted a proposal to the US Bureau of Reclamation (USBR) and according to the review, we need more convincing data from representative wastewater plant. Purolite Co in Philadelphia and Calgon Corporation in Pittsburgh are enthusiastic partners of the project and they will provide their products free of cost. We have a US patent pending on the CO2 induced desalination process. Once proven successful, the process can be rapidly scaled up and implemented in the field. 

Design and Fabrication of Test Membrane Module for Small-Scale, Solar-Driven Membrane Distillation Packages
PI: Alparslan Oztekin
Co-PI(s): Carlos Romero, Nasser Vahedi
University: Lehigh University

Water supply and wastewater treatment is of main concern to society nowadays, as the available water resources are depleting or contaminated, requiring further treatment and purification for use, discharge and reuse. Membrane distillation (MD) is a new water treatment technology at the research and development stage. A hydrophobic membrane inside a membrane module, lets water vapor pass through the membrane to the permeate side, while the liquid phase and all other dissolved or suspended molecules are retained on the feed side. The water vapor transport is driven by the difference in vapor pressure which is a function of a temperature difference. MD operation at low temperatures (45-80 degree Celsius) is perfectly fitted for low grade waste heat utilization or collected solar heat. Development and characterization of a suitable membrane module design for small-scale, solar-driven MD desalination of brackish water is planned for this study.  At Lehigh University, a team of faculty, researchers and students are participating in a research group that aims at developing and optimizing MDs for seawater and industrial wastewater desalination applications. This team has conducted extensive research on simulation and laboratory studies of new water treatment technology and is actively pursuing novel research on MD desalination and wastewater treatment. The proposed research will pursue screening latest membrane materials and configurations for solar-driven desalination applications, considering process efficiency and cost effectiveness for small-scale brackish water (TDS less than 17,000 ppm) treatment applications. The study will consider: potential membrane material screening and membrane configuration selection, membrane module related design, computer simulation and optimization, laboratory-scale membrane module construction and setup, module testing using simulated and actual feed water, performance analysis and module characterization for future scale-up.  The topic and project goals are in line with PITA’s goals and objectives included in the water focus areas of PITA. The proposed research project targets at enhancing the technical capabilities of Lehigh University in this highly active topic area. This area of research has great potential for future research and development in the solar energy and water desalination industries. The proposed study will be conducted with contribution from Solarflux, a solar thermal energy company.  The MD module study results are planned to be used in the future design, development and optimization of a compact commercial water treatment unit for small-scale solar desalination applications.

Understanding the Fate of Contaminants of Emerging Concern and Their Metabolites During Biological Activated Carbon Treatment of Wastewater
PI: John Fox
Co-PI(s): Derick Brown
University: Lehigh University

The purpose of this work is to investigate the fate of Contaminants of Emerging Concern (CECs) and their metabolites across BAC filters.  CECs  include pharmaceuticals, antibiotics and antimicrobial agents, hormones, byproduct compounds formed during water and wastewater treatment, and other trace synthetic organic compounds such as perfluorooctanic acid (PFOA) and perfluorooctane sulfonate (PFOS). These compounds are often present in domestic wastewater and then end-up in drinking water through indirect re-use, by treated wastewater being discharged to rivers and the downstream river water then being used as a water source. As direct reuse of wastewater is being investigated and moving towards application in water scarce regions, the fate of CECs through treatment processes is an important aspect of direct reuse treatment technologies. One process technology which is favorably considered for wastewater treatment for direct and in-direct water reuse is biologically activated carbon (BAC). The benefit of BAC is that both biological and physiochemical methods work in tandem to remove trace organic compounds, such as CECs, from water. However, the current state of knowledge lacks understanding of the fate of CECs and their metabolites across BAC filters. For example, if one CEC enters the BAC it is possible for the biological processes to partially metabolize the parent compound and transform it into another compound, aka the metabolite. In some cases the metabolite of synthetic organic compounds is as toxic or more toxic than the parent compound. Understanding the fate of CECs and their metabolites across BAC filters is necessary to advance water treatment technologies for direct reuse. This work involves a collaboration between Lehigh University, Calgon Carbon Corporation, and the Bethlehem Wastewater Treatment Plant. 

Development of a High-Endurance Bio-Inspired Underwater Vehicle with Energy Extraction Capabilities
PI: Keith Moored
University: Lehigh University

Remotely controlled or autonomous underwater vehicles are becoming more prominent across a wide range of applications.  These “drones” are being used in military operations, for mapping the ocean floor, for inspecting hulls of vessels entering ports, and in exploring remote locations such as under the arctic ice sheet.  The next generation of these devices may be used in waterway management to inspect infrastructure such as the piers of bridges, dams, and locks, for both routine maintenance and during disaster recovery efforts.  To be used in these applications, next generation underwater vehicles should be able to be deployed for extended periods of time, such as over days or months, by having high propulsive efficiency and energy extraction capabilities.  These vehicles should also have high maneuverability and high control authority to navigate precisely in highly dynamic environments such as in shallow waterways.  Bio-inspired underwater vehicles are perfectly suited to address these needs.  The key is to develop a bio-inspired underwater vehicle with (1) a highly efficient propulsive drive and (2) the capability to extract energy from the flow environment to regenerate its batteries.  Bio-inspired devices are also typically more maneuverable and have more precise control authority than conventional underwater vehicles.  Our goal with the proposed work will be to design and develop a highly efficient bio-inspired underwater vehicle that is capable of extracting energy from a flow environment.


Algorithm Development for a Low-Cost, 3D Printed Snake-Like Robot for Inspection and Repair
PI: Subhrajit Bhattacharya
University: Lehigh University

As the facility infrastructure in the United States ages there is a continual need to upgrade safety, electrical, and telecommunication systems. FLX Solutions, Inc., a PA Keystone Innovation Zone and Ben Franklin corporation, has developed a low-cost, 3D printed snake-like robot with inspection, repair, and other infrastructure applications. The robot enables workers to safely perform tasks while helping to improve uptime for companies at a price point that makes it affordable to put in every work truck.

The goal of this project is to continue the development of path planning algorithms that will enable users to easily operate the robot in a number of environments including confined spaces. In a previous PITA, Lehigh University professor Dr. Subhrajit Bhattacharya and his team developed a mathematical and algorithmic framework for autonomous 2D locomotion in maze-like environments like walls. What makes this work unique is that it enables the robot to generate the bracing forces necessary for load-intensive operations such as drilling through studs and joists.

The previously developed and simulated algorithms will be implemented on the physical prototype of the robot being constructed by FLX Solutions, Inc. A feedback controller will be added that fuses information from onboard sensors and cameras to account for mechanical deflections of the robot while under load. The algorithms will then be expanded from 2D to 3D movements. Lastly, methodologies for Simultaneous Location and Mapping (SLAM) using onboard sensors will be developed allowing the robot to operate in any unknown environment without requiring prior mapping information.

This project is a collaboration between research, industry through a partnership between the Mechanical Engineering Department at Lehigh University and FLX Solutions, Inc. The results of the project will enable the snake-like robot to be commercialized revolutionizing the safety and efficiency of upkeep and improvements in infrastructure-related industries.

Smart Banded Rotary Friction Supplemental Damper System for Enhancement of Civil Infrastructure Performance to Mitigate Natural Hazard Effects on Buildings
PI: James Ricles
Co-PI(s): Liang Cao
University: Lehigh University

Unexpected natural hazard events (high-to-extreme wind, earthquake, etc.) and operational natural hazard (low-to-high winds) can cause economic, human, and cultural losses due to structural or nonstructural damage and temporary or long-term inoperability of a building. Energy dissipation systems can be used to enhance the performance of civil structures to these multi-hazard events. In general, there are three types of supplemental control strategies: passive, semi-active, and active. Of these, semi-active devices offer some of the greatest benefits for increasing resiliency because they can modulate their responses to a particular hazard, and unlike active systems, they do not require large energy inputs and do not have the potential to destabilize the structure. The proposed project will outfit a new generation friction damper, called a Banded Rotary Friction Damper (BRFD), with semi-active control features to enhance the performance of buildings subjected to natural hazards that includes forces from wind and earthquake ground motions. Buildings of different geometries and configurations will be outfitted with BRFDs. Using real-time hybrid simulations these structures will then be subjected to wind and earthquake loading to systematically investigate the effectiveness of the BRFD towards improving the resilience of buildings to natural hazards. Real-time machine learning will be used to tune control algorithms that enable the control law to be self-tuning to provide optimal control, making the BRFD autonomous. The proposed project will utilize the resources that exist at the Natural Hazards Engineering Research Infrastructure (NHERI) Lehigh Real-Time Multi-Directional (RTMD) Experimental Facility (EF) housed in the Advanced Technology for Large Structural Systems (ATLSS) Engineering Research Center.

Fire Resistance of Concrete Tunnel Liners with Glazing Materials
PI: Spencer Quiel
Co-PI(s): Clay Naito
University: Lehigh University

A research program is proposed that will examine the impact of surface coatings on reinforced concrete panels that are exposed to fire. The goal of this study is to investigate how tile and paints influence the potential for heat-induced spall damage and permanent concrete degradation to tunnel liners due to significant fire events. The research team will utilize an existing experimental setup at the ATLSS Laboratory to perform high-temperature tests on specimens that are representative of tunnel liner panels both with and without various coatings. The findings of this project will provide guidance to tunnel owners and operators about the fire resistance implications of commonly available and potentially innovative liner coating solutions in tunnels. Both the Pennsylvania Department of Transportation (PennDOT) and Gannett Fleming in Harrisburg, PA have contributed letters of support for this project, the results of which will positively impact their management, maintenance, design, and rehabilitation of tunnel systems in Pennsylvania and across the US.  This project leverages the resources of a Tier 1 university transportation center for underground construction (of which Lehigh is a consortium member) to amplify the national presence of Pennsylvania transportation companies and agencies in the research and development of resilient and sustainable tunnel infrastructure.

Managing Power Infrastructure Under Wildfire Risk
PI: Paolo Bocchini
University: Lehigh University

Lehigh University and OSIsoft will partner to conduct a preliminary study on wildfire risk associated with power line failures caused by strong winds. In the past few years millions of acres were burned by these fires, billions of dollars were lost, and hundreds of civilians lost their lives in these accidents. As a preventive measure, power companies are operating “public safety power shutoff” event. However, to avoid liability, power companies have very low thresholds to trigger these blackouts, possibly too low. In the last planned blackout more than 3 million people and commercial activities remained without power, with obvious negative social and economic consequences.

OSIsoft and Lehigh want to join forces to build a comprehensive model of wind, vegetation, power line topology, and structural characteristics of the power line components, to determine accurately the risk of starting fires. These models can be used when a strong wind event is imminent, to determine if a preventive blackout is actually warranted. Moreover, they can be used irrespective of imminent events to identify the power line segments at highest risk, and allocate effectively the limited resources to put these lines underground.

Lehigh’s researchers already have expertise in catastrophe modeling, risk assessment, and regional infrastructure analysis. OSIsoft can facilitate the data acquisition and processing (especially the real-time processing when there is an imminent event). This would improve the state of Pennsylvania as it would impact jobs in PA as OSIsoft moves to support this dramatic change in the power sector for California and other areas to follow.


Characterization of Polymers Solutions and Colloids Suspension in a Temperature Field
PI: Xuanhong Cheng
University: Lehigh University

Polymer Colloids can be produced by emulsion polymerization as well as other approaches, and they have broad industrial applications such as coatings, adhesives, home and personal care, medicine, biotechnology, and energy. In addition, the shift from solvent to waterborne dispersions has yielded large and measureable sustainability benefits.  Despite great advances in analytical technologies, it remains challenging to characterize the details of polymer colloids with different composition and in formulated systems. The proposed research will address the challenge by combining the expertise of the Cheng group at Lehigh University in nanoparticle separation, and the strength and needs of Dow in colloidal sample characterization. Specifically, we propose to develop new analytical methods for the characterization of suspended colloids based on their migration in a temperature field, which is complementary to existing approaches that is dependent on single parameters such as particle size, charge, density and chemistry. Directional migration of solute species under a temperature gradient, termed thermophoresis, occurs ubiquitously in nature. The migration speed of is dictated by many factors, including particle size, surface charge, particle-solvent interaction, ionic double layer, absolute temperature as well as the temperature gradient. Since solvent-solute interactions as well as the solute species themselves both play critical roles in thermophoresis, characterization of the resulting movements could be used as a quality control tool to monitor global characteristics of colloid suspensions. In addition, coupling a temperature gradient with a microfluidic channel is proposed to separate and retrieve species of different thermophoretic velocities. The separated fractions can be further characterized by conventional analytical methods to provide details about the chemical composition of the original sample. Thus, proposed research will offer new capabilities to analyze complex colloid samples for quality control purposes. Furthermore, the study will advance fundamental understanding of the thermophoresis phenomena in various colloid systems.