Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1406&sort=-id
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-id", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1424&sort=-id", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1407&sort=-id", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1405&sort=-id" }, "data": [ { "type": "Grant", "id": "513", "attributes": { "award_id": "2029640", "title": "Collaborative Research: A virtual workshop on conducting language research online: Enhancing the resilience of the language sciences in a time of social distancing", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)" ], "program_reference_codes": [], "program_officials": [ { "id": 1051, "first_name": "Tyler", "last_name": "Kendall", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-06-01", "end_date": "2021-05-31", "award_amount": 10115, "principal_investigator": { "id": 1052, "first_name": "Joshua R de", "last_name": "Leeuw", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 278, "ror": "https://ror.org/022x6qg61", "name": "Vassar College", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 278, "ror": "https://ror.org/022x6qg61", "name": "Vassar College", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "This project supports a five-week long virtual workshop for language scientists. The purpose is to train language scientists to conduct language studies with human subjects over the Internet. There are two primary motivations. First, in response to the COVID-19 crisis, laboratories around the world have ceased in-person human data collection. That means ideas being left untested, discoveries not being made, students not getting trained, and human talent left untapped. Second, Internet-based experiments offer a number of advantages for research: they can be cheaper, faster, provide better data, and allow researchers to work on questions that are impossible to study in the lab. In fact, a number of researchers have pointed out that language science research would be advanced if more studies could be undertaken using Internet-based data collection. Thus, by enabling researchers to rapidly move research online, this project will not only help mitigate the costs of the COVID-19 crisis, but will result in a science that is more robust and faster-moving than before the crisis. The workshop will be free and open to all. All materials will be available online for individuals who could not attend the live workshop. In each of the first four weeks, there will be a 2 to 3 hour live video presentation, including a live question and answer (Q&A) session. Each presentation will be followed several days later by a live, message board-based Q&A. During the fifth week, there will be an additional message board-based Q&A. Topics will include technical skills, such as using popular software platforms, as well as other research implementation skills, such as handling ethics issues and subject recruitment. The workshop will be facilitated by the teams experienced in the design and use of two robust software packages for online experiments.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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "512", "attributes": { "award_id": "2032772", "title": "Group Support Grant for IEEE/CVF CVPR 2020 Doctoral Consortium", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)" ], "program_reference_codes": [], "program_officials": [ { "id": 1049, "first_name": "Jie", "last_name": "Yang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-06-15", "end_date": "2021-05-31", "award_amount": 22500, "principal_investigator": { "id": 1050, "first_name": "Richard", "last_name": "Souvenir", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 277, "ror": "https://ror.org/00kx1jb78", "name": "Temple University", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 277, "ror": "https://ror.org/00kx1jb78", "name": "Temple University", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "This award supports the participation of students from US-based institutions in the Doctoral Consortium at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020, which will be held virtually due to COVID-19. CVPR is the premier annual conference in computer vision with over 9000 participants from all over the world. The award helps the career development of some of the brightest junior researchers in computer vision to increase the number of active researchers and educators in STEM and ensures that the computer vision community, through its recent graduates, makes fast advances in solving problems that will benefit society as a whole. The Doctoral Consortium aims to have representation from a diverse group of participants in terms of gender, ethnic background, academic institution, and geographic location.NSF support covers some of the costs for 40 selected US-based graduate students to participate in the Doctoral Consortium and CVPR conference. The Doctoral Consortium highlights the work of senior PhD students who are within six months of receiving their degrees (including recent graduates) and gives these students an opportunity to discuss their research and career options with faculty and researchers who have relevant expertise and experience. The opportunity to receive advice on their research work and career plans from experts from different institutions is particularly important due to the uncertainty of job prospects and disruption of traditional networking and recruiting events caused by COVID-19. Participants and recipients of support are selected by the 2020 CVPR Doctoral Consortium Chair. The support covers admissible conference-related costs such as registration. This year's Doctoral Consortium features a poster session, one-on-one mentoring, and panel discussion, all held virtually. These components were introduced in past doctoral consortiums and were well received.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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "511", "attributes": { "award_id": "2029414", "title": "RAPID/Collaborative Research: Developing Pandemics and Healing Models for Coronavirus COVID-19 to Assist in Policy Making", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)" ], "program_reference_codes": [], "program_officials": [ { "id": 1047, "first_name": "Ann Von", "last_name": "Lehmen", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-06-01", "end_date": "2022-12-31", "award_amount": 80000, "principal_investigator": { "id": 1048, "first_name": "Houman", "last_name": "Homayoun", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 276, "ror": "", "name": "University of California-Davis", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 276, "ror": "", "name": "University of California-Davis", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "The current pandemic has stimulated a strong response on the part of local, state, and federal government, with containment largely achieved through stringent lockdowns, effectively quarantining nearly every household in the country. Given the enormous socio-economic impacts of this approach, it is imperative to understand how to minimize the spread of the epidemic while also minimizing deleterious effects and maximizing the availability of critical health resources. This project seeks to address this challenge by devising a better and scalable alternative to lockdown under suitable constraints.This project focuses on developing models for the COVID-19 pandemic, in particular looking at neighboring community spread, mitigation measures, and optimal distribution of healthcare resources in that context. This project aims to (i) devise a better and scalable alternative to full lockdown; (ii) devise a cognitive solution that can be applied to various demographics having heterogeneous connectivity and population distribution with minimal information regarding previous epidemic spread; and (iii) minimize the impact of epidemic model uncertainties on the confinement and medical resource allocation strategies. The PIs will employ a collection of novel mathematical techniques to the problem that can handle heterogeneity and are scalable. The interdisciplinary team includes Johns Hopkins University, which has been a major Center for the collection of COVID-19 data.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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "510", "attributes": { "award_id": "2034045", "title": "RAPID-REU Site: Mitigating the Impact of COVID-19 Pandemic on Undergraduate Research Training in the Biosciences", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)" ], "program_reference_codes": [], "program_officials": [ { "id": 1044, "first_name": "Sally", "last_name": "O'Connor", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-06-15", "end_date": "2022-05-31", "award_amount": 75042, "principal_investigator": { "id": 1046, "first_name": "Margaret J", "last_name": "Eggers", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 259, "ror": "https://ror.org/02w0trx84", "name": "Montana State University", "address": "", "city": "", "state": "MT", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1045, "first_name": "Colin A", "last_name": "Shaw", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 259, "ror": "https://ror.org/02w0trx84", "name": "Montana State University", "address": "", "city": "", "state": "MT", "zip": "", "country": "United States", "approved": true }, "abstract": "The COVID-19 pandemic has caused an unprecedented disruption in the research training of undergraduates thru the Research Experience for Undergraduates (REU) Sites program. Massive cancellations of REU Sites caused undergraduates to be deprived of both the intellectual and professional development opportunities they would have received in an REU program. The situation is further aggravated by the closures of similar undergraduate internship programs sponsored by other Federal and private agencies. The lack of meaningful summer jobs due to the pandemic provides little alternatives for college students. The situation is particularly acute for minority students whose underrepresentation in STEM has been a continuing challenge for the Nation. This RAPID proposal helps to mitigate the impact of the COVID-19 pandemic on Native American and other underrepresented minority students. Research opportunities in environmental biosciences, including research in COVID-19 related area, will be made available to students in the regional areas surrounding Montana State University. The projects have been designed so that students can conduct the research remotely. Professional development training will be done virtually thru regularly scheduled events, similar to what the students would receive in an in-person training program. This RAPID proposal will engage underrepresented minority students in research thru projects that can be conducted remotely. Research projects include the quantification of SARS-CoV-2 virus in a managed wastewater treatment system in Montana, where preliminary studies have shown that viral load can be detected using a standard CDC test kit protocol for SARS-CoV-2 virus. The metagenomic analysis of the water samples can be used as an indicator of community spread of the virus and can become one of the surveillance tools for public health officials. Other projects involve research that would benefit American Indian reservations in Montana, such as the ecological study of the impacts and benefits of bison reintroduction on the Blackfeet reservation, studies on biodiversity, ecosystem services and crop quality on managed farms on reservation lands, and the role of microbial communities in the phytoremediation of arsenic in soil. Students will be trained on the analysis of data and use of modeling tools. Students will be recruited from tribal colleges and other Montana universities and colleges. Student professional development will emphasize ethics and the responsible conduct of research, careers in STEM, and effective scientific communication. Students present their research at a virtual conference at the end of the 10-week program. This project is supported by the Division of Biological Infrastructure and the Established Program to Stimulate Competitive Research (EPSCoR).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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "509", "attributes": { "award_id": "2008456", "title": "III: Small: Data-Driven Control of Epidemic Processes over Complex Dynamic Networks", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)" ], "program_reference_codes": [], "program_officials": [ { "id": 1042, "first_name": "Amarda", "last_name": "Shehu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-06-01", "end_date": "2023-05-31", "award_amount": 439893, "principal_investigator": { "id": 1043, "first_name": "VICTOR M", "last_name": "PRECIADO", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 232, "ror": "https://ror.org/00b30xv10", "name": "University of Pennsylvania", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 232, "ror": "https://ror.org/00b30xv10", "name": "University of Pennsylvania", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "Despite notable advances in medicine over the last century, recent pandemics such as COVID-19 remind us that the threat of infectious diseases to human populations is very real. While continuing advances in medicine are essential, information technologies can greatly improve our ability to detect and contain the devastating effects of infectious diseases. In this direction, public health agencies collect, periodically update, and publicly report field data containing geolocated information about the tested, infected, recovered, hospitalized, and deceased individuals in those areas affected by the disease. However, this data is unreliable, incomplete, and coarse-grained; therefore, health agencies can greatly benefit from information technologies to filter and analyze field data in order to make reliable predictions about the future spread of the disease. Moreover, the final objective of a health agency is to use this information to design efficient strategies to contain the spread of infectious diseases. To achieve this objective, health agencies have at their disposal epidemic-control resources, such as social distancing, traffic restrictions, and the distribution of pharmaceutical resources (whenever available). Due to the heterogeneity and high cost of these resources, finding the cost-optimal allocation of each type of resource throughout the population is a very challenging problem of utmost societal impact. In this project, we propose to develop an integrated framework for modeling, prediction, and cost-optimal control of epidemic outbreaks using finite resources and unreliable data.In order to implement practical epidemic-control tools, it is necessary to first develop mathematical models able to replicate salient geo-temporal features of disease transmission. These patterns are strongly influenced by the geography of the area over which the disease is spreading, as well as the mobility patterns of the population. In this direction, we will use complex contact graphs to model both realistic geographical constraints and mobility patterns. In particular, the vertices of this graph correspond to towns/districts and its links represent interactions between them. On top of this contact graph, we will build a dynamical model aiming to replicate the complex geo-temporal spread of the disease. In this direction, we will consider a system of stochastic processes, coupled through the edges of the contact graph, to model the evolution of the disease. Once the model of the spread is tuned, we will then proceed to the design of a coordinated strategy to contain the spread of the infection by distributing resources throughout the population. In this direction, we will design and implement an optimization program to find the cost-optimal allocation of heterogeneous resources given a finite budget. In this research task, we must deal with the inherent uncertainty of field data, as well as the presence of sampling biases that can have a dramatic impact on the fairness of the cost-optimal allocation of resources. The success of the proposed research program would greatly improve our ability to efficiently detect and appropriately react to epidemic outbreaks, whereupon a rapid control response can be deployed.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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "508", "attributes": { "award_id": "2032481", "title": "Virtual Workshops for GEO REU Students and PIs During COVID-19", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)" ], "program_reference_codes": [], "program_officials": [ { "id": 1040, "first_name": "Elizabeth", "last_name": "Rom", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-06-01", "end_date": "2021-05-31", "award_amount": 48449, "principal_investigator": { "id": 1041, "first_name": "Valerie F", "last_name": "Sloan", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 275, "ror": "", "name": "University Corporation For Atmospheric Res", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 275, "ror": "", "name": "University Corporation For Atmospheric Res", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true }, "abstract": "In response to the COVID-19 national emergency, many Research Experiences for Undergraduates (REU) Site programs have cancelled their programs for the summer 2020 due to a lack of housing and travel restrictions. Some REU sites that are funded by the Division of Ocean Sciences have created virtual internships for students who will be able to access and analyze data remotely. The University Corporation for Atmospheric Research (UCAR) will organize an on-line professional development workshop that is designed to support about sixty undergraduates and faculty mentors who are participating in virtual REU internships during the summer 2020. This professional development workshop series will ensure that students who are participating in the virtual internships have the professional development opportunities that are normally part of an in-person REU program.This project will support a workshop series that will focus on two topics. 1. Student Professional Development: providing a series of weekly professional development seminars for students participating in the virtual REUs. These seminars will serve the dual purpose of developing a sense of cohort amongst students, and 2. Faculty Support and Materials: supporting faculty mentors in preparing to run a virtual REU, providing faculty with seminars on facilitating professional development topics, and providing some webinar recordings and materials for guided student activities. The project will also be a model for future virtual internship programs.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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "507", "attributes": { "award_id": "2031245", "title": "EAGER/Collaborative Research: Experimentally Validated Modeling of the Dynamics of Carbon Dioxide Removal from the Bloodstream via Peritoneal Perfluorocarbon Circulation", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [], "program_officials": [ { "id": 1038, "first_name": "Harry", "last_name": "Dankowicz", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-06-01", "end_date": "2022-05-31", "award_amount": 150000, "principal_investigator": { "id": 1039, "first_name": "Joseph S", "last_name": "Friedberg", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 262, "ror": "", "name": "University of Maryland at Baltimore", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 262, "ror": "", "name": "University of Maryland at Baltimore", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true }, "abstract": "This EArly-concept Grant for Exploratory Research (EAGER) project brings together a multidisciplinary team of control engineers, biomedical engineers, medical researchers, and clinicians to explore a novel pulmonary-independent method for supplementing gas exchange in an animal. Specifically, the research team will study whether the circulation of oxygenated perfluorocarbon (PFC) through the abdomen (the peritoneal cavity) of a large animal, can serve as a pathway for clearing carbon dioxide (CO2) from the animal’s bloodstream; and what are the governing dynamics of this CO2 clearing process. The peritoneal cavity essentially acts as a “third lung” in this scenario, providing critical life support for patients whose compromised lung function has exceeded the support achievable through mechanical ventilation. There is currently a critical need for this treatment, within the context of the COVID-19 pandemic, but this system also has potential to emerge as a standard modality in the critical care of hundreds of thousands of patients in pulmonary failure. Furthermore, the medical community will benefit from the deep fundamental understanding of the CO2 removal capabilities of peritoneal oxygenated PFC circulation, which will be an essential element in bringing this technology into future clinical trials.This project addresses the challenge of building an experimentally validated model of the dynamics of carbon dioxide transport from the bloodstream of a large animal into oxygenated perfluorocarbon perfused through the animal’s abdominal (peritoneal) cavity. Using the experimental data obtained as part of this project, the team will develop and parameterize a control-oriented, multi-compartment model of the transport dynamics governing CO2 removal. While previous experiments on peritoneal oxygenated PFC circulation have predominantly examined quasi-steady conditions, the research team will ensure the richness of its data by deliberately designing the underlying experiments to maximize the identifiability of the CO2 removal dynamics. The result will be a dataset better suited for the modeling and estimation of underlying system dynamics than the quasi-steady datasets. The system dynamics and control community will benefit from the opportunity to apply its scientific tools and methods to the dynamic modeling of a novel ventilation technology. Particularly important is the degree to which such modeling can help broaden the interdisciplinary impact of the dynamic systems and controls discipline to a new health-related application technology. Addressing this research challenge urgently, but rigorously, has the potential to provide critical assistance to the medical research community, particularly considering the COVID-19 crisis.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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "506", "attributes": { "award_id": "2030282", "title": "EAGER-Development of Antiviral Functionalized Carbon Nanotubes (CNTs) for Generating Virus-free Medical Grade Water and Preventing the Spread of COVID-19", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [], "program_officials": [ { "id": 1036, "first_name": "Nora", "last_name": "Savage", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-06-01", "end_date": "2023-05-31", "award_amount": 138573, "principal_investigator": { "id": 1037, "first_name": "Somenath", "last_name": "Mitra", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 228, "ror": "https://ror.org/05e74xb87", "name": "New Jersey Institute of Technology", "address": "", "city": "", "state": "NJ", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 228, "ror": "https://ror.org/05e74xb87", "name": "New Jersey Institute of Technology", "address": "", "city": "", "state": "NJ", "zip": "", "country": "United States", "approved": true }, "abstract": "At this point there is an urgent need to address many issues related to the Corona virus (COVID-19) outbreak including stopping the spread of the virus, and helping the overwhelmed healthcare industry cope with different problems. The goal of this project is to generate antiviral functionalized carbon nanotubes which will have several applications related to the COVID-19 pandemic. First, the functionalized nanotubes can be used as self-cleaning sorbents in personal protection equipment (PPE) such as medical masks. Many commercial paints/coatings formulations contain carbon nanotubes, and the anti-viral nanotubes will be effective materials for preventing the spread of COVID-19 via surface contacts. An immediate application is carbon nanotube-enhanced membrane distillation for inexpensive bacteria/endotoxin/virus-free medical-grade water generation. This water is used for cleaning medical equipment and as injectable water in patient treatment. Most importantly the membrane distillation with antiviral nanotubes will be developed into a point-of-care technology where a domestic water heater or a microwave oven can be used to generate medical grade water in field hospitals. The Objective of this project is to develop functionalized carbon nanotubes with high anti-viral activity for stopping the spread of Covid-19 and helping the overwhelmed healthcare industry. The work will be an extension of work already underway with bacteria and endotoxins. The two major proposed tasks are the development of specific carbon nanotube functional forms with antiviral activity, and the development of carbon nanotube enhanced microwave induced membrane distillation for generating bacteria/endotoxins/virus free medical grade water. In the first task, functionalized carbon nanotubes will be synthesized by the incorporation of different antiviral agents. In the second task these nanotubes will be used to synthesize biocidal membranes for membrane distillation. In the proposed process, as the hot contaminated water will pass over the antiviral nanotube membrane, it will be partially transformed to water vapor that will pass through as purified water while the hydrophobic membrane will prevent the aqueous phase from permeating through. In this project we also propose to use microwaves to heat the water because this has additional biocidal effects. The novel membranes developed by immobilizing the antiviral carbon nanotubes will not only serve as molecular transporters for pure water generation, but also have biocidal properties that will generate highly pure medical grade water. The developed approach will open the door to specific carbon nanotube functionalization to deal with different bacteria and virus. These functionalized nanotubes can be used in adsorbents and membranes to provide effective virus protection in air purifiers, in personal protection equipment (PPE) such as gas masks and for water treatment. Since many commercial paints/coatings formulations contain carbon nanotubes, the anti-viral nanotubes in surface coating will serve to prevent the spread of COVID-19 (or other viruses) that transmit via surface contacts. The educational goal of the project is the expansion of nanotechnology into disease prevention and medical infrastructure, which has not been emphasized in the past but has significant potential benefits.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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "505", "attributes": { "award_id": "2030859", "title": "Computing Innovation Fellows 2020 Project", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)" ], "program_reference_codes": [], "program_officials": [ { "id": 1030, "first_name": "Mitra", "last_name": "Basu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-05-15", "end_date": "2024-04-30", "award_amount": 15936600, "principal_investigator": { "id": 1035, "first_name": "Ellen W", "last_name": "Zegura", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 274, "ror": "https://ror.org/00agrkd75", "name": "Computing Research Association", "address": "", "city": "", "state": "DC", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1031, "first_name": "Mark D", "last_name": "Hill", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1032, "first_name": "Andrew", "last_name": "Bernat", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1033, "first_name": "Elizabeth", "last_name": "Bradley", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 1034, "first_name": "Ann W", "last_name": "Schwartz", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 274, "ror": "https://ror.org/00agrkd75", "name": "Computing Research Association", "address": "", "city": "", "state": "DC", "zip": "", "country": "United States", "approved": true }, "abstract": "The current coronavirus (COVID-19) pandemic is disrupting aspects of daily life and work, including having a serious impact on the current faculty-recruiting season in the computing-research community. A Computing Research Association (CRA) survey completed on April 1st 2020 counted about 100 academic-computing job positions being pulled from the market due to hiring freezes, and universities continue to forecast major financial losses for the upcoming academic year, with likely negative impacts on the academic-computing job market for the next year as well. Thus what is needed is a bridge that keeps highly trained researchers in the academic pipeline to preserve future computing innovation and to meet the training needs of future computing professionals, as these will be the backbone of the future economy. CRA and its Computing Community Consortium (CCC) provided such a bridge for the severe economic downturn a decade ago, using NSF funding to administer three cohorts of Computing Innovation Fellows (CIFellows). That postdoctoral project kept 127 young scholars in research with career-enhancing programs. The current project, CIFellows 2020, is intended to provide similar support for the academic-computing pipeline in light of the damage it is sustaining in the wake of the current pandemic. The CIFellows 2020 project takes inspiration from the original CIFellows project but adapts it to the current uncertain situation, by incorporating more flexibility, allowing the option of doing a postdoc at the applicant’s current institution, and providing a significant mentoring/cohort-building component that is based on best practices that emerged from the original effort. Fellows may come from any research area under the umbrella of NSF Computing and Information Science and Engineering (CISE). Fellows will engage in a 1-2 year postdoctoral experience that furthers their career development in new ways. An application process will be implemented, and selection of successful applicants will be made using a technical program-committee style with strict adherence to conflicts of interest and based on a holistic evaluation of merit and diversity along many dimensions, with major emphasis on intellectual merit and broader impacts in applicant materials.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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "504", "attributes": { "award_id": "2025693", "title": "Ecology of MERS-CoV in camels, humans, and wildlife in Ethiopia", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)" ], "program_reference_codes": [], "program_officials": [ { "id": 1028, "first_name": "Katharina", "last_name": "Dittmar", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2019-08-25", "end_date": "2023-08-31", "award_amount": 2308740, "principal_investigator": { "id": 1029, "first_name": "Amira", "last_name": "Roess", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 239, "ror": "https://ror.org/02jqj7156", "name": "George Mason University", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true }, "abstract": "Zoonotic diseases are diseases that exist in animals and can be transmitted to humans. Zoonoses currently account for approximately 75 percent of all emerging infections worldwide. However, the factors that trigger zoonotic disease emergence and spread remain poorly understood, in part because they involve multiple species, complex inter-species and intra-species relationships, and many interacting environmental, behavioral, social, and economic factors. Middle Eastern Respiratory Syndrome-Coronavirus (MERS-CoV) presents a case in point. MERS-CoV was first identified in human beings in 2012 and has killed 36 percent of those infected, but little is known about what has led to its emergence. For example, several human cases have been traced to contact with camels imported from Africa; studies also have detected MERS-CoV in African wildlife; and while camels are a known reservoir host for MERS-CoV, it is not known if there are additional reservoirs or intermediate hosts, or how frequently and under what conditions MERS-CoV spillover occurs. This project will investigate the natural ecology of MERS-CoV in relation to broader social, economic, and environmental changes; its potential wildlife reservoirs in close contact with camels; and the potential for spillover to humans who consume, herd, and trade camels and camel products. This work is of direct importance to national security with respect to disease threats; MERS-CoV is currently on the World Health Organization's priority shortlist of diseases in urgent need of accelerated research. To understand when and under what conditions MERS-CoV emerges and spreads from animals to humans, the researchers ask, (1) How do social, cultural and behavioral characteristics of camel economics shape virus ecologies? (2) Which intra- and inter-species interactions increase MERS-CoV emergence and transmission? and (3) What climatic and environmental variables are associated with transmission? Researchers will conduct field studies of wild and domestic (camel) reservoirs, collecting roughly 800 samples from targeted wildlife (ungulates and eulipotyphlads) over two years at four locations. They will carry out socio-behavioral studies across the camel value chain and follow individual camels longitudinally to determine when the same individuals seroconvert to MERS-CoV positive status. They will conduct laboratory analyses and do viral sequencing. The researchers will integrate the data into mathematical models using the 'Method of Plausible Parameter Sets' (MPPS), which will determine which mechanistic scenarios are consistent with observed patterns of MERS-CoV in camels and humans. This broadly inclusive approach expands upon traditional studies of zoonotic disease emergence. The transmission models will be applicable to other zoonotic diseases linked to livestock production and will help to identify interventions to reduce disease emergence and transmission.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.", "keywords": [], "approved": true } } ], "meta": { "pagination": { "page": 1406, "pages": 1424, "count": 14236 } } }