Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1385&sort=-keywords
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-keywords", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1419&sort=-keywords", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1386&sort=-keywords", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1384&sort=-keywords" }, "data": [ { "type": "Grant", "id": "10437", "attributes": { "award_id": "2204082", "title": "Collaborative Research: Transport of model-virus through the lung liquid lining", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "FD-Fluid Dynamics" ], "program_reference_codes": [], "program_officials": [ { "id": 573, "first_name": "Ron", "last_name": "Joslin", "orcid": null, "emails": "", "private_emails": null, "keywords": "[]", "approved": true, "websites": "[]", "desired_collaboration": "", "comments": "", "affiliations": [] } ], "start_date": "2022-04-01", "end_date": "2025-03-31", "award_amount": 214373, "principal_investigator": { "id": 26435, "first_name": "Juan M", "last_name": "Lopez", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": "[]", "approved": true, "websites": "[]", "desired_collaboration": "", "comments": "", "affiliations": [ { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 147, "ror": "https://ror.org/03efmqc40", "name": "Arizona State University", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true }, "abstract": "The novel coronavirus SARS-CoV-2, responsible for the COVID-19 pandemic, is similar to other respiratory coronaviruses, such as SARS-CoV (2002) and MERS-CoV (2012). All these viruses cause dangerous respiratory disorders with high mortality and grave impacts on society. This virus destroys the cells that produce lung surfactants which, among other things, keep the alveoli air-sacks from collapsing, and eventually the lungs fill with liquid. The fluid dynamic interactions between the liquid lining of the lung, the lung surfactants, and the respiratory virus are presently not well understood. This project addresses this gap by conducting experiments and numerical modeling to capture the essential fluid dynamics of a model virus interacting with lung surfactant. The numerical models will then be used to simulate physiologically relevant scales, not accessible experimentally. In addition to understanding flow in the lung liquid lining, the present knowledge gap in predictive modeling of interfacial dilation and compression is hampering developments in other areas, such as in water waves, which are of utmost importance in modeling of carbon dioxide gas exchange between the atmosphere and the oceans.\n\nThe two primary functions of lung surfactants are regulating the interfacial tension and surface viscosities of the liquid lining of the alveoli, and providing a first line of immune defense against airborne pathogens. Predictive models for the transport of small particles in a surfactant-covered liquid layer will be developed. A key advancement in the proposed modeling of surface elasticity is to measure the equation-of-state of the monolayer in a state corresponding to that found when it has been subjected to a large number of dilation/compression cycles. The usual approach of determining properties of a recently spread monolayer undergoing slow compression is inappropriate for modeling monolayer hydrodynamics coupled to an oscillatory bulk flow, as the monolayer is in a different state with very different interfacial properties. The role of interfacial dilatational viscosity and its significance relative to surface elasticity remains poorly understood and presents a major impediment to the predictive modeling of free-surface flows. The PIs have a proven track record of productive multidisciplinary collaboration, and will continue to provide a unique educational opportunity for the graduate and undergraduate students.\n\nThis 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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "10438", "attributes": { "award_id": "2203262", "title": "EAGER: A Holistic Heterogeneous Temporal Graph Transformer Framework with Meta-learning to Combat Opioid Epidemic", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Info Integration & Informatics" ], "program_reference_codes": [], "program_officials": [ { "id": 864, "first_name": "Sylvia", "last_name": "Spengler", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-11-01", "end_date": "2023-09-30", "award_amount": 150000, "principal_investigator": { "id": 638, "first_name": "Yanfang", "last_name": "Ye", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 171, "ror": "https://ror.org/00mkhxb43", "name": "University of Notre Dame", "address": "", "city": "", "state": "IN", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 171, "ror": "https://ror.org/00mkhxb43", "name": "University of Notre Dame", "address": "", "city": "", "state": "IN", "zip": "", "country": "United States", "approved": true }, "abstract": "The devastating and lethal opioid epidemic has largely been fueled with various opioids in the United States. Unfortunately, driven by considerable profits, opioid trafficking has co-evolved with modern technologies, such as, social media platforms have been utilized for marketing and selling illicit drugs including opioids, which has attracted increasing attention from both public health agencies and law enforcement. As online opioid trafficking activities are nimble and resilient, it calls for novel techniques to effectively detect opioid trades to facilitate proactive response strategies. By advancing capabilities of machine learning and data science, the goal of this project is to design and develop a holistic framework to model and analyze dynamic multi-modal data to fight against online opioid trafficking and, thus, help combat opioid epidemic. This research will enable a conceptual framework for the federal and state governments, public health agencies, law enforcement, and local communities to develop proactive strategies to build up a drug-free world - one community at a time. \n\nBy engaging novel disciplinary perspectives, this exploratory, yet transformative, high risk-high payoff work will involve radically different approaches for the development of an integrated framework to combat online opioid trafficking. The research will have three key components. First, the team will propose a novel heterogeneous temporal graph (HTG) to comprehensively model and abstract multi-modal posts and relational information over time on social media. Second, based on the constructed HTG, the research team will develop an innovative graph transformer to learn user representations for opioid trafficker detection. Third, to tackle the challenge of lack of sufficient labeled data for model training, the team will further develop a new meta-learning algorithm by joining unsupervised graph structure and small amount of supervised training data to update the model. This will enable the model to quickly adapt to a new task, such as identifying a new type of traded opioid and its traffickers on social media, using only a few samples and training iterations. The developed holistic framework for the detection of online opioid trafficking activities will have significant impacts on addressing the critical national opioid epidemic facing our society. The research will be beneficial to data mining and machine learning communities, as well as multidisciplinary domains such as public health, epidemiology, social and behavioral sciences. The outcomes of this project will be made publicly accessible and broadly distributed. The project will integrate research with education through novel curriculum development, participation of underrepresented groups, and student mentoring activities.\n\nThis 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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "10439", "attributes": { "award_id": "2112345", "title": "STTR Phase I: A DLT Machine Learning Platform for Blockchain Warehousing", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "STTR Phase I" ], "program_reference_codes": [], "program_officials": [ { "id": 670, "first_name": "Anna", "last_name": "Brady", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-03-15", "end_date": "2022-11-30", "award_amount": 255916, "principal_investigator": { "id": 26436, "first_name": "Mohammad", "last_name": "Sadoghi", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1933, "ror": "", "name": "MOKA BLOX LLC", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this Small Business Technology Transfer (STTR) project improves the use of large-scale databases, data warehouses, and the software that used to commercially data-mine these resources. In the era of big data, many applications, such as machine learning and artificial intelligence, critically rely on data functionalities including efficiency, interoperability, and analysis. However, their data subsystems are challenged to meet these needs due to multiple technical limitations, such as centralized storage, homogeneous data formats, and tightly coupled workflows. This STTR project will develop a new framework to overcome these limitations to improve big data applications in various scientific fields, such as biological sciences, astronomy, and computational chemistry. At the end of this Phase I project, the requisite knowledge and foundational materials for developing a blockchain-based data warehousing middleware will be produced.\n\nThis STTR Phase I project proposes to advance the specific knowledge and commercialization of DLTs (Distributed Ledger Technologies) and blockchains by creating novel and innovative environments for further development for DLTs and blockchains as a \"virtuous cycle\", starting with bottlenecks identified in modern data warehouses. The DLT tool developed here advances blockchain protocols, specifically for removing quadratic and speculative costs from orthodox protocol-based approaches to blockchains on conventional hardware and instituting linearizable and constant costs with other innovative programming methods (e.g., probabilistic pruning). New statistical, topological, and computational approaches will be researched and developed for these purposes, including for development of techniques applicable for machine learning, artificial intelligence, peta-scale and exa-scale computing, advanced scientific computing, and pedagogical academic development of future generations. The expected results include a new set of protocols and a unified tool for integrating data types and multiple networks into conventional data warehouses, especially for advancement of data warehouses struggling to keep pace with new blockchain data, metadata, and real-time analytics.\n\nThis 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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "10440", "attributes": { "award_id": "2225756", "title": "RII-BEC: Transcending Barriers to Success in Economics for Underrepresented Students: Preparing COVID-Affected Students for Their Climate-Resilient Future", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Office Of The Director", "EPSCoR Research Infrastructure" ], "program_reference_codes": [], "program_officials": [ { "id": 10733, "first_name": "Andrea", "last_name": "Johnson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-10-01", "end_date": "2027-09-30", "award_amount": 999986, "principal_investigator": { "id": 26383, "first_name": "Robert", "last_name": "Franco", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 26382, "first_name": "Denise E", "last_name": "Konan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 684, "ror": "", "name": "University of Hawaii", "address": "", "city": "", "state": "HI", "zip": "", "country": "United States", "approved": true }, "abstract": "This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). \n\nIn Hawaii, the COVID-19 pandemic is having a disproportionate impact on Native Hawaiian, Pacific Islander, and Filipino communities, and on women. These four groups are also extremely underrepresented in the field of economics at the University of Hawai‘i at Manoa. At the same time, environmental challenges that are further exacerbated by climate change threaten these islands, wider Oceania, and coastal communities in Asia and the Americas. This project creates a new economics bridge between Kapiolani Community College (KCC) and the University of Hawaii at Manoa (UHM) to prepare and transfer 100 students from these disproportionately COVID-affected groups into baccalaureate and graduate level economics degree programs. The project weaves indigenous and western knowledge systems and community engagement strategies to contextualize economics coursework to bridge associate, baccalaureate, and master’s degree programs. Active learning, peer mentoring, engaged research, and internship opportunities will enhance the urgency and relevance of economics coursework so that students can embrace and ameliorate the challenges of biocultural restoration and climate resilience in their neighborhoods, communities, regions, and world. The project will promote the progress of science by connecting key concepts and practices from indigenous science with economics curricula, instruction, and research. Further, the project will serve the national interest by amplifying indigenous voices and values, promoting biodiversity conservation and mixed economy and community enterprise models that contribute to nutrition, health, well-being, climate resilience, income generation and prosperity for all American households.\n\nThe project goal is to develop, implement and evaluate a bridge program in economics between KCC and UHM for 100 students from disproportionately COVID-affected groups as they and their communities transition from COVID-affected to climate-resilient and prosperous. The first project objective is to make indigenous and western knowledge system connections for redesigned curriculum and enhanced learning opportunities in first- and second-year economics courses at KCC and five BA and MA leading summer bridge courses at UHM. The Leadership Team will implement a 5-year faculty development program to create new curricular materials, instructional methods, and active learning opportunities, including service, research, and internships. Students in the summer bridge courses will conduct research on the grand challenges of biocultural restoration and climate change. This research can be further developed and advanced in third- and fourth-year and graduate courses. As this objective is met, the project will also develop student recruitment, mentoring, retention and learning strategies that will help these students gain a strong sense of belonging in college, becoming an economics major and a growing sense of reciprocity and responsibility in community and careers. The project will build authentic, durable intra- and inter-campus and campus-community partnerships that increase student well-being and program health, and close indigenous and female degree completion gaps in economics. The project has six deliverables: 1) a KCC-UHM Transfer and Articulation Agreement; 2) an eight course sequence in economics (with course syllabi) across 2-year, 4-year and graduate programs; 3) a handbook on community-based active learning opportunities for underrepresented students; 4) a handbook on integrating and advancing research in urban and regional planning, sustainability and resilience, and economic futures; 5) an “Indigenizing Economics” concept paper, and 6) a network improvement communications plan for climate resilience and economic prosperity.\n\nThis 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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "10441", "attributes": { "award_id": "2235570", "title": "I-Corps: Digital tool against post-traumatic stress disorder among COVID-19 survivors", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "I-Corps" ], "program_reference_codes": [], "program_officials": [ { "id": 602, "first_name": "Ruth", "last_name": "Shuman", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-08-01", "end_date": "2023-01-31", "award_amount": 50000, "principal_investigator": { "id": 26384, "first_name": "Spyros", "last_name": "Kitsiou", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 163, "ror": "https://ror.org/02mpq6x41", "name": "University of Illinois at Chicago", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this I-Corps project is the development of a cost-effective, convenient, and time efficient solution to address post-traumatic stress disorder (PTSD). The public health crisis following the trauma of COVID-19 requires new solutions to increase healing and improve outcomes. This technology seeks to connect patients with an anonymous community forum, eye movement desensitization reprocessing, meditation, and yoga. Core algorithms will be used to assess treatment options for post-traumatic stress disorder.\n\nThis I-Corps project is based on the development of software to facilitate healing of post-traumatic stress disorder (PTSD), while decreasing the cost of care and improving outcomes of those suffering from PTSD. The COVID-19 pandemic has created trauma, disability, and death in the U.S. The incidence of post-traumatic stress disorder (PTSD) incidence related to COVID-19 is approximately 30% of the U.S. population. This technology seeks to advance a core set of algorithms that diagnosis patients, determining if they are positive for PTSD and improving their awareness of treatment options. The approach involves an agile methodology that emphasizes iteration and implementation of continuous feedback from the patient. The proposed innovation involves software that may help those navigating post-traumatic stress disorder through a set of core algorithms to minimize barriers and improve access to resources. This technology may be able to decrease costs associated with diagnosis and improve the ease with which healthcare is provided at a location that the patient prefers, such as at home.\n\nThis 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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "10442", "attributes": { "award_id": "2221469", "title": "Engineering Academic Pathways", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Unknown", "S-STEM-Schlr Sci Tech Eng&Math" ], "program_reference_codes": [], "program_officials": [ { "id": 1223, "first_name": "Alexandra", "last_name": "Medina-Borja", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-10-01", "end_date": "2028-09-30", "award_amount": 1499608, "principal_investigator": { "id": 26388, "first_name": "Brett", "last_name": "Tempest", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 26386, "first_name": "Stephanie N", "last_name": "Galloway", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26437, "first_name": "Catherine", "last_name": "Blat", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26438, "first_name": "Sejal", "last_name": "Foxx", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 248, "ror": "https://ror.org/04dawnj30", "name": "University of North Carolina at Charlotte", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "This project will contribute to the national need for well-educated scientists, mathematicians, engineers, and technicians by supporting the retention and graduation of high-achieving, low-income students with demonstrated financial need at the University of North Carolina at Charlotte, an urban, access-oriented institution. Over its 6-year duration, this project will fund scholarships to 25 unique full time students who are pursuing bachelor’s degrees in Civil Engineering, Mechanical Engineering, Systems Engineering, and Engineering Technology. Eligible scholars will be able to receive up to four years of support while they complete their undergraduate degree. A suite of evidence-based programming will be deployed to enhance opportunities for social-emotional learning, academic skills development, and social and navigational capital building that were missed due to the pandemic. The project makes an urgent, evidence-based response to pandemic impacts on low-income students’ preparation for and enrollment of engineering majors, as well as their missed opportunities for social and emotional learning. Key components of programing include a summer bridge program, high engagement mentoring, a college skills and professional development seminar, and dedicated advising. The programing will improve employment prospects by developing social and cultural capital in students. Through outreach, the program will also help large numbers of high school students learn about engineering majors and prepare them for the college application process and will train high school counselors about engineering opportunities for low income students.\n\nThe Engineering Academic Pathways program is specifically designed to enhance the prospects of economic mobility by responding to the unique needs of low-income students that the pandemic has substantially exacerbated. Recent data indicate the pandemic has disproportionately harmed people in low-income households relative to employment, health, and well-being. Prior to the setbacks of the COVID 19 pandemic, Charlotteans were responding to substantial disparities in opportunity after the city was ranked 50th out of the 50 largest US cities for economic mobility in 2015. The program will implement four of the strategies for improving economic mobility that were recommended by the Charlotte-Mecklenburg Opportunity Task Force in 2017. First is to broaden the range of and access to high quality college and career pathways offered by K-12 and postsecondary institutions. Second is to equip all students and their parents with the information and guidance they need to understand and navigate multiple college and career pathways, preparation, and processes. Third is to expand and strengthen support for First Generation and other low-socioeconomic students who need help transitioning to and completing secondary education. Fourth, and finally, is to elevate and actively promote the critical importance of acquiring a post-secondary degree. The success of individual elements of the program will be rigorously evaluated and adapted for the greatest effectiveness. This will advance understanding of the unique needs of low income students in a post-pandemic world and enable the dissemination of best practices through professional development seminars and scholarly publications to other institutions that are reacting to similar conditions. This project is funded by NSF’s Scholarships in Science, Technology, Engineering, and Mathematics program, which seeks to increase the number of low-income academically talented students with demonstrated financial need who earn degrees in STEM fields. It also aims to improve the education of future STEM workers, and to generate knowledge about academic success, retention, transfer, graduation, and academic/career pathways of low-income students.\n\nThis 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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "10443", "attributes": { "award_id": "2223678", "title": "EFRI ELiS: Living Microbial Sensors for Real-Time Monitoring of Pathogens in Wastewater", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "EFRI Research Projects" ], "program_reference_codes": [], "program_officials": [ { "id": 1123, "first_name": "Mamadou", "last_name": "Diallo", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2023-01-01", "end_date": "2026-12-31", "award_amount": 1999271, "principal_investigator": { "id": 26395, "first_name": "Rafael", "last_name": "Verduzco", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 5795, "first_name": "Lauren B", "last_name": "Stadler", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26394, "first_name": "Caroline M", "last_name": "Ajo-Franklin", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26439, "first_name": "Jonathan", "last_name": "Silberg", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26440, "first_name": "Kirstin R.W.", "last_name": "Matthews", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 357, "ror": "", "name": "William Marsh Rice University", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "SARS-CoV-2 is the virus that causes COVID. It can be detected in wastewater. Its detection can act as a signal to a community that the infection is spreading locally. The goal of this project is to develop living sensors that can continuously monitor wastewater for the presence of SARS-CoV-2. Living microbial sensors are robust and low-cost. They can regenerate themselves and can be engineered to detect a specific biomolecular target of interest. The modular design can be easily repurposed to detect and monitor a variety of chemical and biological targets in the environment. Training undergraduate, graduate, and postdoctoral researchers will advance the development of a competitive bioeconomy workforce. The project will also establish new K-12 outreach programs in collaboration with Houston-area public schools. Enhancing current programs that offer research opportunities to community college students and K-12 teachers is another objective. Engaging the public and relevant stakeholders to address ethical, legal, and social implications of living microbial devices is another important aspect of this project.\n\nDevelopment and deployment of living microbial sensors is the overall objective of this project. These sensors will be based on engineered electroactive microorganisms. Addressing broader societal challenges related to the potential adoption of engineered microbial devices, including safety, legal, and regulatory concerns is another important aspect of the project. Several fundamental science and engineering challenges must be met to make such devices. Establishing methods for engineering microbes that can directly detect large macromolecules, such as the SARS-CoV-2 spike protein is one. Developing scalable methods for processing engineered microorganisms into functional biohybrid materials is another. Designing compact and low power devices that can amplify electronic signals delivered by the electroactive microbes is a third. Ultimately, evaluating the stability and performance of these devices in different environmental settings, including wastewater, will be critical to establishing the efficacy of these devices The project team will also identify and conduct in-person semi-structured interviews with vested stakeholders such as regulators, public health experts, infectious disease specialists, and environmental advocates. The interviews will identify major public concerns and regulation that could impede implementing the proposed bioelectronic technology. Altogether, this work will provide a solid foundation and analysis for understanding, developing, and translating living microbial sensors as real-time and low-cost environmental sensors.\n\nThis project is jointly sponsored by the National Science Foundation, Office of Emerging Frontiers and Multidisciplinary Activities (EFMA) and the Department of Defense – Defense Threat Reduction Agency (DTRA).\n\nThis 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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "10444", "attributes": { "award_id": "2229267", "title": "FMSG: Eco: Off-Grid Construction via Sustainable Compression Curing of Vegetable Oil-Impregnated Sediments", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "FM-Future Manufacturing" ], "program_reference_codes": [], "program_officials": [ { "id": 1065, "first_name": "Joy", "last_name": "Pauschke", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-10-01", "end_date": "2024-09-30", "award_amount": 494685, "principal_investigator": { "id": 26400, "first_name": "Scott", "last_name": "Thompson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 26396, "first_name": "Rafael L", "last_name": "Quirino", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26399, "first_name": "Genevieve S", "last_name": "Baudoin", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26441, "first_name": "Karin", "last_name": "Goldberg", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26442, "first_name": "Dean", "last_name": "Snelling", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 197, "ror": "https://ror.org/05p1j8758", "name": "Kansas State University", "address": "", "city": "", "state": "KS", "zip": "", "country": "United States", "approved": true }, "abstract": "Additive manufacturing (AM) has effectively revolutionized how engineers and architects design and fabricate products due to its layer-by-layer building approach. New levels of product complexity/customization not offered by traditional manufacturing processes are now achievable, resulting in weight reduction, enhanced conformability, joint consolidation, and higher efficiencies through design. This project combines faculty in engineering, chemistry, architecture, and geology to innovate a solar-powered compression/curing technique that additively fabricates building materials made of tung oil and local sands for sustainable, raw-earth construction. This manufacturing method can leverage available natural resources within the U.S., therefore reducing any reliance on international raw materials. It also responds to a growing need to innovate and overcome remote construction constraints exacerbated by urban-to-rural migration driven by the COVID pandemic and climate change. The remote AM of raw earth materials will help reduce the large carbon footprint associated with concrete-based AM construction which relies on heavy gantry-based material extrusion systems that must be transported to worksites. Architecture students will be trained on a commercial binder-jet AM system for integrating new knowledge in sustainable AM processes into their designs. Guest lectures will be provided to engineering and architecture undergraduate students to broaden their perspectives and creativity to ensure future innovation in the U.S. advanced manufacturing industries.\n\nThe goal of this fundamental manufacturing research project is to design and test a new binder/powder-based AM process for the fabrication of earth-sourced composites for structural applications. Through modeling and experimentation, the AM process will be designed for off-grid use while remaining completely sustainable. Tung oil will be employed for binding sands of highly variable sizes, shapes, and chemistry. Employed sands will be characterized using microscopy and flowability measurements. These measurements will be correlated with the sediment’s ability to spread into a thin layer with minimal voids when acted upon by a custom-designed roller. Binder rheological properties will be varied until effective jetting and sediment infiltration are realized. The binder will be cured via free radical polymerization triggered by a combination of heat and ultraviolet (UV) radiation. The latent heat required for uniform binder curing in the presence of unrefined sediments will be related with concentrated solar energy/spectra for aiding the design of a solar power/heating unit. First order energy balances and entropy minimization will guide power/heating unit design. A proof-of-concept manufacturing system will be constructed and instrumented for conducting “brick” building experiments. Thermomechanical tests will be performed to determine the strength of these manufactured composite bricks.\n\nThis project is jointly funded by the Division of Civil, Mechanical, and Manufacturing Innovation and the Established Program to Stimulate Competitive Research (EPSCoR).\n\nThis 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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "10445", "attributes": { "award_id": "2141473", "title": "EAGER: Compact Field Portable Biophotonics Instrument for Real-Time Automated Analysis and Identification of Blood Cells Impact Impacted by COVID-19", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "EPMD-ElectrnPhoton&MagnDevices" ], "program_reference_codes": [], "program_officials": [ { "id": 26401, "first_name": "Leon", "last_name": "Shterengas", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-09-01", "end_date": "2024-08-31", "award_amount": 220000, "principal_investigator": { "id": 26402, "first_name": "Bahram", "last_name": "Javidi", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 257, "ror": "https://ror.org/02der9h97", "name": "University of Connecticut", "address": "", "city": "", "state": "CT", "zip": "", "country": "United States", "approved": true }, "abstract": "COVID-19 pandemic quickly overwhelmed the healthcare resources in even advanced economies with large scale global fatalities not seen since the Spanish Flu of 1918. This project intends to investigate the impact of the COVID-19 virus on human red blood cells using an automated low-cost, field portable bio-photonics instrument. These studies can lead to better understanding of the impacted blood cells and precise measurement of cell anomalies for potential early detection of COVID-19. Accurate, rapid, and low-cost analysis and diagnosis of COVID-19 from blood cells with a compact field portable bio-photonics instrument interfaced with mobile devices will be a substantial advance toward widespread testing, medical diagnosis, early detection, disease prevention, and relevant data collection, particularly in remote areas without access to dedicated healthcare facilities. The proposed cross disciplinary project is based on a transformative biophotonics sensing approach for real-time analysis and disease detection and offers an alternative to conventional labor- and resource- intensive bio-molecular approaches. This analysis and capability would enable medical researchers to study and gain increased understanding of the effects of COVID-19 infections on blood cells. The proposed approach may provide a fast and reliable testing mechanism with the potential for widespread deployment, which is critical in dealing with pandemics, such as COVID-19, with high rates of infection and mortality. The success of the proposed approach would allow for automated low cost, rapid and highly accurate assessment of the impact of COVID-19 on blood cells, which is not currently possible using conventional methods. The proposed research provides new capabilities and benefits including real-time sensing and diagnosis; early detection with high accuracy, specificity, and sensitivity, and low cost field portable deployment in under resourced healthcare systems for real-time monitoring of pandemics.\n\n\nInvestigating the impact of COVID-19 on blood cells and making detailed real-time measurements of the COVID-19 induced changes and anomalies of the blood cells at sub-micron scales would provide valuable research insights to fight COVID-19 and future pandemics. The proposed approach employs computational multi-dimensional sensing and imaging at sub-micron scales to analyze morphology and motility of blood cells. Specially embedded algorithms are integrated with mobile devices to analyze opto-biological signatures of blood cells in real time to find potential clues to the impact and presence of COVID-19 for rapid (real-time) COVID analysis and detection. The measurements and analysis of the infected cells will be performed at sub-micron scale lateral resolution and nano scale longitudinal resolution. The proposed project investigates blood cells morphology and temporal motility quantitatively with high precision using high resolution self-referencing digital holographic in compact 3D-printed platforms. Multidimensional bio-optical signature data, including spatial structure, refractive index, stiffness, and dynamic temporal behavior of the blood cells will be investigated to understand the influence of COVID-19 in blood cells. The use of dedicated machine learning algorithms associated with the analysis of anomalies in blood cells due to COVID-19 are intended to produce accurate detection and analysis.\n\nThis 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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "10446", "attributes": { "award_id": "2133170", "title": "Collaborative Research: Optimized Testing Strategies for Fighting Pandemics: Fundamental Limits and Efficient Algorithms", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "CCSS-Comms Circuits & Sens Sys" ], "program_reference_codes": [], "program_officials": [ { "id": 26403, "first_name": "Huaiyu", "last_name": "Dai", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-09-01", "end_date": "2025-08-31", "award_amount": 274774, "principal_investigator": { "id": 26404, "first_name": "Jing", "last_name": "Yang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 219, "ror": "", "name": "Pennsylvania State Univ University Park", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "Large-scale high-throughput prevalence and diagnostic testing is essential for the containment and mitigation of pandemics. The testing bottleneck in the COVID-19 pandemic has led to a resurgence of interest in group testing, where several people's biological samples are mixed together and examined in a single test. When the rate of infection in the population is low, this method can significantly reduce the total number of tests per subject and increase the throughput of the existing testing infrastructure. However, traditional group testing has the following limitations: First, efficient group testing based methods for the estimation of prevalence have been largely overlooked in the literature. Second, traditional group testing usually assumes that the testing results are qualitative (positive versus negative), not quantitative (providing viral load information). Third, the theoretical study of group testing rarely takes practical constraints, such as the sensitivity of the pooled tests and the dilution effect, into consideration, which hinders the applicability of the testing schemes in practice. The goal of this project is to overcome these limitations of traditional group testing and design advanced pooled testing strategies for efficient prevalence tracking and accurate infection diagnosis. It will develop optimized pooled testing strategies with strong theoretical performance guarantees yet feasible and cost-effective in practice.\n\nThe proposed research is organized in three research thrusts as follows. Thrust 1 aims to design effective sampling and testing algorithms to estimate the prevalence in communities and track its evolution, under scarce testing resource constraints. Thrust 2 focuses on the design of optimized pooling and decoding algorithms for compressed sensing based (COVID-19) virus diagnostic testing. Thrust 3 validates the accuracy and efficiency of the proposed pooled testing through experiments on anonymized COVID-19 samples. This project bridges group testing and online learning, the two largely disconnected areas, with the objective to effectively allocate limited testing resources for efficient prevalence tracking. Such integration leads to novel sampling strategies, broadens the paradigm of group testing, and advances the state of the art of online learning. Moreover, the proposed compressed sensing based diagnostic testing leverages quantitative measurements provided by advanced testing technologies, which can significantly increase test throughput, reduce the number of needed tests, decrease the consumption of scarce reagents, and provide results robust against observation noises and outliers. The rich compressed sensing theory provides possible approaches to the rigorous mathematical certification of the correctness of the decoded results. Besides, the clinical constraints on pooled testing also lead to novel problem formulation and theoretical characterization, enriching the study of compressed sensing.\n\nThis 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.", "keywords": [], "approved": true } } ], "meta": { "pagination": { "page": 1385, "pages": 1419, "count": 14184 } } }