David Beck, PhD - US grants

The purpose of this proposal is to gain new insights into several biochemical and transport properties of cerebral endothelium in vitro under normal conditions and following co-culture with other brain cell types. All experiments will be performed utilizing cells derived from mouse cerebromicrovasculature. Specifically, we propose to 1) examine the cytochemical localization of several hydrolytic enzymes in cerebral endothelium in vitro utilizing an in vitro blood brain barrier model and determine the influence of glial cells on the localization of these hydrolytic enzymes on cerebral endothelial membranes, 2) examine the transport of water across cerebral endothelial membranes and the effect of various sulfhydryl-reactive agents on permeability of cerebral endothelial cells to water, 3) examine the effect of cortiscosteroids on the transport of water across cerebral endothelial cell membranes, and 4) examine the effect of corticosteroids on anionic charge distribution oni cerebral endothelial cell membranes. The information obtained from these studies should extend our understanding of the normal function of the blood-brain barrier. It should provide new information on the movement of water across the blood-brain barrier, and finally, it should provide insight into the mechanism of corticosteroid control of brain edema.

This Integrative Graduate Education and Research Traineeship (IGERT) award provides Ph.D. students at the University of Washington with multidisciplinary training in computer science, statistics, and domain sciences (oceanography, astronomy, chemistry, and genome sciences). Through this blended approach, trainees will learn how to manage, analyze, and visualize increasingly large amounts of data (known as ?Big Data?), thereby being prepared to address the challenges of cyberinfrastructure in the 21st century.

Intellectual Merit: By developing a new Ph.D. program that involves partnerships with 11 leading companies and national labs in the field of Big Data, this program provides trainees with a collaborative approach to processing, scaling, and modeling massive and complex data sets for the scientific community. Trainees learn to create new statistical and machine learning techniques needed to manage large data sets. Additionally, this program builds an open-source system for scientists worldwide to access and analyze Big Data through a Cloud service.

Broader Impacts: This IGERT traineeship program aims to create a new Big Data curriculum that will be delivered online and through University of Washington outreach initiatives. The program also prepares a new generation of scientists with the interdisciplinary tools to approach problems that will arise in the field of cyberinfrastructure. Moreover, this program promotes the development of a diverse STEM workforce by recruiting and training underrepresented groups, women, and students with disabilities, particularly through a partnership with the AccessComputing Alliance and the University of Washington DO-IT program.

IGERT is an NSF-wide program intended to meet the challenges of educating U.S. Ph.D. scientists and engineers with the interdisciplinary background, deep knowledge in a chosen discipline, and the technical, professional, and personal skills needed for the career demands of the future. The program is intended to establish new models for graduate education and training in a fertile environment for collaborative research that transcends traditional disciplinary boundaries, and to engage students in understanding the processes by which research is translated to innovations for societal benefit.

The University of Washington will host a national workshop where graduate students in data science disciplines will interact to explore data science grand challenges in a collaborative environment. The project will implement a novel idea and advance the understanding of how to develop data science communities by engaging graduate students, academia, and industry. It also addresses an important national need for researchers with cross-disciplinary training in data science and serves as a catalyst for other institutions to integrate data science skills more broadly throughout the curriculum.

The goal of the project is to help create a highly connected workforce that is adept at cross-disciplinary communication and idea synthesis, primed to solve a new generation of data science and big data challenges. To accomplish this goal, the investigators will conduct a 2.5-day workshop for approximately 100 attendees that is designed to (1) introduce graduate students to new data science concepts and applications, (2) enable students to interact with experts from industry, domain, and methodology fields, and (3) initiate the establishment of a professional community using team building activities. The investigators will conduct a pre-workshop promotional campaign and develop a web site where users can connect and share their ideas. Following the workshop, a set of white papers, team reports, and a workshop report will be available to the public.

The manufacturing life cycle begins with the discovery of new molecules and materials. This first step is often initiated through computer simulations that explore the space of possible molecules and materials, and identify promising candidates that can later be tested in laboratories. As simulations have grown in scale and complexity, this step has become a critical bottleneck. New data-driven approaches present the opportunity to increase the speed and accuracy of such predictions, with broad potential impact on the US Manufacturing sector. This Harnessing the Data Revolution Institutes for Data-Intensive Research in Science and Engineering (HDR-I-DIRSE) Frameworks award brings together Engineers and Data Scientists to conceptualize a new Engineering Data Science Institute where these tools can be applied for new discovery. The effort will develop new data science approaches to accelerate the engineering life cycle: design, characterization, manufacturing, and operation. This life cycle starts with the discovery of new molecules and materials, followed by advanced characterization with high throughput methods augmented by machine learning. Then, efficient manufacturing and operation of systems that use these materials can be designed and developed. By focusing on this holistic lifecycle, the researchers will build a broadly applicable foundation in Engineering Data Science methods. The new Institute will seek to create an Engineering Data Science environment that supports engineers and scientists (students, postdoctoral researchers, and faculty) through a synergistic set of collaboration and education activities.


This collaborative effort follows three thrusts. The first focuses on the reduction of the experimental design space with data science tools targeting the discovery of new molecules and polymers. The research develops a new, formal framework for pairing accurate predictive simulations with data-driven models to create a scalable and transferable workflow that can be deployed across multiple examples of molecular engineering applications. The second thrust addresses a manifold of cross-cutting needs at the intersection of image data analytics and characterization of materials and systems. It also builds community cyberinfrastructure through open-source software resources with support for execution in public clouds. The final thrust focuses on improving manufacturing, optimization, and control. It further enhances cyberinfrastructure resources through a suite of open-source software solutions to systematically develop digital twin models for complex engineering and manufacturing systems, and apply them for optimization and control. This project is part of the National Science Foundation's Harnessing the Data Revolution (HDR) Big Idea activity and is co-funded by the Office of Advanced Cyberinfrastructure.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Redox flow batteries (RFBs) are large-scale, high capacity energy storage systems. RFBs work by flowing liquids through cells to insert (i.e. charging) or to extract energy (i.e. discharging) from the system and then storing the liquids in large reservoirs. These types of large-scale batteries enable the use of renewable energy technologies, such as solar and wind power, that may not always generate peak energy output coincident with demand. Significant improvements in energy storage capacity, power output and material costs are still needed to enable wide-scale deployment of RFB technology. This research project will utilize advanced electrochemical tools, robotics and computational methods to rapidly accelerate progress in developing the next generation of liquids as RFB energy storage materials. Students involved in the project will interface with another NSF-funded project on data science, which will allow them to take specialized courses in data science and machine learning that are focused on energy applications. This project also will engage students at all levels in research activities and help to train future engineers and scientists to tackle future problems in energy.

This research program will use high-throughput electrochemical, spectroscopic and physicochemical techniques along with data-enabled discovery and bench-top RFB performance evaluation to discover new electrolyte materials with improved energy storage capacity. Specifically, the work focuses on redox-active deep eutectic solvents (RDES) produced from novel combinations of organic redox active molecules, hydrogen bond donors, and organic salts. Because of their organic nature, RDES electrolytes can be produced from abundant and inexpensive raw materials (e.g. dyes) while at the same time increasing the maximum attainable cell potentials. High-throughput analytical tools will be used to efficiently sample relevant properties over a large molecular design space and converge on viable RDES formulations. Experimental data sets will then be analyzed through the application of advanced data science algorithms to identify the chemical and formulation parameters that most effectively correlate to properties and to optimize electrolyte formulations. Lastly, lab-scale RFB tests will evaluate performance metrics to determine the viability of these RDES electrolytes in large-scale energy storage applications.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

A workshop, titled "Catalyzing Electrochemical Data Sciences: Education and Research Opportunities," will be held at the Spring 2021 Electrochemical Society (ECS) Meeting in Chicago, with contingencies for a potential virtual meeting. The workshop will focus on data sciences for the electrochemical community. A key objective will be to identify research priorities and strategies for promoting open electrochemical data repositories and software. Such systems will accelerate research progress in electrochemical science and engineering. To assure a diversity of participation at the workshop, participants will be nominated by the steering committee and by relevant ECS Division Executive Committees. The two-day agenda includes a series of presentations on the creation and analysis of labeled datasets in electrochemical science and engineering. The agenda also contains seminars on open source software of significance to the electrochemical community. Discussions will be guided by steering committee participants, and a report on priorities and research gaps will be drafted by steering committee members. An associated educational activity, entitled Hackweek, will be aimed at the electrochemical software developer community through online teaching and project work.

Analysis, aggregation and use of different electrochemical datasets from multiple labs are limited by a lack of standardized file structures, a lack of data quality standards, and a lack of long-lived, scalable, and searchable open data repositories. Extensive dataset archives are critical to machine learning, statistical analysis methods, and validation and parameter estimation in physics-based modeling. The lack of such data slows progress in electrochemical fields. The objective of this workshop is to identify promising research directions in data science focused on providing accessible repositories of electrochemical data for the research community. This workshop will ascertain research gaps in the processes of storing and integrating big data in electrochemical research. Members of the steering committee will lead critical activities of the workshop including selecting participants and drafting the report. The workshop leaders have significant experience in educational and management aspects of the workshop as well as in research topics pertaining to data science.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Pollution discharged by industrial processes impacts human health and the health of ecosystems, and mitigating their effects requires considerable cost in time, effort, and dollars. This project will use advanced instrumentation to characterize contaminants in the environment and biological systems, enabling the design of appropriate mitigation strategies. It will support undergraduate, graduate, and professional education and training in environmental and analytical chemistry, environmental engineering, environmental health, and data science; and (3) foster new collaboration and community engagement opportunities, especially with the regional Native American communities, local and state government agencies, and industries impacting stormwater quality. UW-Tacoma is a primarily undergraduate institution, a non-PhD granting institution, an urban serving, a Carnegie community engaged, and an Asian American and Native American Pacific Islander Serving Institution. It has a student body comprised of many underrepresented minorities, veterans, and first-generation college students.

The system to be acquired is a Liquid Chromatograph-High Resolution Mass Spectrometer, specifically an Agilent 6546 UPLC-QTOF-HRMS instrument. The instrument will be used to understand and improve management of various forms of pollution, especially for stormwater and roadway systems, innovative treatment materials development, ecotoxicology and bioassay development, and water disinfection. For example, the instrument will be used to identify toxic transformation products from stormwater and quantify sources. Another use is to study the oxidation of persistent organic compounds in urban stormwater using ferrate-coated sand media and PFAS defluorination. Yet another study focuses on the fate of organic pollutants in the aquatic environment and their occurrence and impacts in the marine environment. With so many potential environmental pollutants, high throughput, analytical capacity, and reliability are critical limiting factors to research effectiveness. Because of the richness, depth and breadth of the data generated, screening techniques employing high resolution mass spectrometry have now become key methodologies for environmental chemistry and engineering studies.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.