Internal & Seed Grants

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Funding Source: RU-N Chancellor’s Office

Amount: $50,000 each (8 grants awarded)

Original Request for Proposals: 2026ChancellorsSeedGrant_wMemo.pdf

Brief Overview: The Chancellor’s Seed Grant Program is a strategic investment designed to accelerate Rutgers University–Newark’s research and creative works expansion as we advance toward our rightfully claimed Carnegie R1 designation. It’s designed to foster collaboration and innovation at the intersections of disciplines. By innovating at these intersections, we create and apply new knowledge and solutions that address complex societal challenges. This program reflects a commitment to community-engaged R1 ambitions, leveraging the collective strength of Rutgers University as a member of several Big Ten Alliances (academic, research, financial, staff, student success) while amplifying the distinctive contributions of the Rutgers University-Newark (RU-N) campus. Priority was given to research and creative works that position RU-N at the forefront of innovation and societal impact.

Review Process: The Provost’s Office, with the Office of Research and Sponsored Programs, convened a committee of 7 faculty/PhDs from across RU-N to review all seed grant proposals received. At least 2 people reviewed each proposal independently before all of them were discussed as a group. Through this process, the committee recommended finalists to the Chancellor. Eight proposed projects were funded in February 2026.

 

Below is a summary of the funded projects in 2026, in no particular order

 

Data-Driven Discovery of Next-Generation Liquid Electrolyte Energy Materials with Tuned Electrochemical Stability

Michele Pavanello, SASN, Department of Physics
Huixin He, SASN, Department of Chemistry

This project aims to accelerate discovery of high-energy-density liquid electrolytes for next-generation batteries and supercapacitors by combining advanced quantum chemistry simulation, multiscale modeling, and machine learning. The research team will develop and validate a novel DFT embedding computational method (using the eDFTpy platform) to accurately calculate the electrochemical stability window (ESW) of complex electrolyte mixtures—one of the key constraints limiting energy storage performance. The significance lies in overcoming long-standing computational barriers that prevent reliable prediction of electrolyte behavior in realistic multi-component systems, enabling faster, cheaper, and more precise materials discovery than traditional trial-and-error experimentation. Experimental electrochemical measurements will be used to validate and refine the simulations, producing high-quality datasets that can train ML models to screen and predict patentable, industry-leading electrolyte formulations. The seed grant will generate proof-of-concept results positioning the team for a major NSF DMREF proposal and a scalable electrolyte discovery pipeline with broad industrial relevance.

 

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The Rutgers Newark Science Bus

Vanessa LoBue, SASN, Department of Psychology
Kristina Keating, SASN, Earth and Environmental Science

This project proposes the creation of a “Newark Science Bus,” a mobile research lab and classroom designed to bring Rutgers–Newark science directly into Newark communities, reducing barriers to participation in research and STEM learning. The proposal is significant because it addresses two major national challenges: the lack of diverse research samples in psychology/neuroscience and the persistent underrepresentation of Black and Hispanic students in STEM, especially in Earth and Environmental Sciences. By enabling data collection and educational programming off-campus, the bus will strengthen community trust, expand research inclusion, and create hands-on opportunities for underrepresented students and residents to engage with science. The project leverages existing Rutgers expertise in mobile labs and includes clear evaluation metrics to measure impact on recruitment, representation, and community engagement. Ultimately, the Newark Science Bus will serve as long-term infrastructure that enhances research competitiveness and supports future NSF proposals focused on broadening participation in STEM.

 

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Disabled Futures: The Anti-Eugenic, Reparative Action Lab

Alison Howell, SASN, Political Science
Lauren Shallish, SASN, Urban Education
Chris Agans, SCJ, New Jersey Scholarship for Transformative Education in Prisons
Javier Robles, SASN, Department of Kinesiology
Nia Tuckson, SCJ, New Jersey Scholarship for Transformative Education in Prisons

This proposal seeks to establish Rutgers University–Newark as a national interdisciplinary research hub focused on disability in higher education, with a specific emphasis on the historical legacy of eugenics and universities’ responsibility to pursue reparative scholarship and disability justice. The project will pilot two major research programs: (1) the first empirical study designed to demonstrate the educational benefits of disability in higher education, and (2) the first systematic reparative study examining the role of land-grant universities (including Rutgers) in advancing eugenic ideology and practices. The work is significant because it links a largely unexamined institutional history to present-day inequities in education, incarceration, and social exclusion—especially in New Jersey, where racialized disability disparities are severe. Through archival research, student-centered training programs, and the development of new research instruments, the project aims to generate publishable findings and position RU-N for major external funding while advancing institutional accountability and disability inclusion.

 

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Neural noise during reading as a mechanism of autism

William W. Graves, SASN, Department of Psychology
Miriam Rosenberg-Lee, SASN, Department of Psychology
Michael W. Cole, SASN, Center for Molecular and Behavioral Neuroscience

This project investigates the neural basis of reading differences in autism by testing whether adults with autism show distinct brain mechanisms during reading despite often achieving comparable reading performance to neurotypical peers. Building on the “neural noise hypothesis” of autism, the study will use fMRI to examine whether autism-related differences emerge in how the brain represents written language—from small units like letters and graphemes to larger units like whole words and meaning. Using a highly controlled reading-aloud task and advanced analytic methods (representational similarity analysis and connectivity-based activity flow modeling), the research will compare neural representation and information flow patterns between autistic adults and a well-characterized neurotypical dataset. The significance lies in identifying mechanistic brain-based explanations for autism-related cognitive differences that could improve understanding of learning processes and guide more effective interventions. The project is designed as high-impact pilot work that can support a future NIH R21 submission focused on autism neurobiology and cognition.

 

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Terahertz emission spectroscopy and theoretical study of chirality-induced spin-selectivity mechanisms

Elena Galoppini, SASN, Chemistry Department
Neepa Maitra, SASN, Physics Department

This project proposes a new experimental and computational approach to uncover the mechanisms of chirality-induced spin selectivity (CISS)—a phenomenon in which chiral molecules act as efficient non-magnetic spin filters with major implications for next-generation spintronics and optoelectronics. The team will develop chromophore–chiral bridge–metal systems anchored to heavy-metal films and use terahertz emission spectroscopy (TES) to detect ultrafast spin-polarized electron transfer via spin-to-charge conversion, creating a capability that has not previously been applied to molecular CISS systems. The significance lies in providing a direct, quantitative probe of spin currents that could resolve long-standing theoretical gaps, since existing models have struggled to explain experimentally observed levels of spin selectivity. The project integrates molecular synthesis, ultrafast THz spectroscopy, and advanced time-dependent density functional theory simulations (including electron–nuclear coupling) to identify mechanistic trends and optimize molecular designs. Seed funding will generate proof-of-concept results supporting an NSF-CHE collaborative proposal and longer-term DOE funding focused on light-driven molecular spintronics.

 

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Revolutionizing Earlier Alzheimer’s Detection: An Age-Agnostic, Non-Invasive Cognitive Mapping and Topological Approach Unaffected by Cultural and Educational Barriers

Bernadette A. Fausto, SASN, Center for Molecular & Behavioral Neuroscience Rutgers
Touseef Haider, SASN, Department of Mathematics & Computer Science
Kimele Persaud, SASN, Department of Psychology

This project proposes a novel, age-agnostic and culturally inclusive approach to early Alzheimer’s detection by combining web-based cognitive testing with advanced brain network analysis. The team will validate the Rutgers Generalization Tasks as a scalable tool that measures fluid cognitive processes less influenced by education and “cognitive reserve,” addressing major limitations of traditional memory-based screening tools. The project will also integrate diffusion tensor imaging (DTI) with topological network measures of white matter connectivity to examine whether cognitive performance corresponds with early, subtle changes in brain network efficiency. The significance lies in developing a non-invasive, lower-cost, and more accessible strategy for identifying preclinical Alzheimer’s risk before major brain atrophy occurs—particularly in diverse urban populations like Newark. Seed funding will generate critical pilot reliability/validity evidence and neuroimaging correlations to support future NIH-scale proposals aimed at transforming early detection and prevention of Alzheimer’s disease.

 

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Human and Computer Relations: A Global Performance and Sound Lab

Alexandra Chang, Price Institute; American Studies; SASN, Dept of Art, Culture & Media
Keary Rosen, Form Design Lab; Express Newark; SASN, Dept of Art, Culture & Media
Kate Doyle, SASN, Music; Department of Arts, Culture & Media; American Studies
Anonda Bell, Paul Robeson Galleries
Eva Giloi, SASN, Dept of History, Global Urban Studies; American Studies

This proposal seeks seed funding to expand the Global Performance and Sound Lab into a globally connected interdisciplinary research and creative platform focused on human–computer relations in the arts. The project will convene Rutgers–Newark faculty and international collaborators across fields such as performance, sound studies, digital mapping, AR/VR, cybernetics, and AI to develop new research collaborations, experimental creative works, exhibitions, and practice-based curriculum initiatives. The significance lies in positioning Rutgers–Newark as a visible leader in emerging arts-and-technology scholarship at a moment when digital and immersive tools are rapidly reshaping how art is created, studied, and taught. Seed funding will support a major June 2026 convening and strengthen networks needed to launch future external grant proposals and long-term infrastructure, including potential PhD-level and center development. The project is designed to generate scalable outputs—creative productions, publications, curriculum development, and grant-ready partnerships—targeting major funders such as Terra, Mellon, and the Asian Cultural Council.

 

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Transforming Digital Attention into Social Action: A Multimodal AI Framework for Digital Philanthropy

Jin Ai, School of Public Affairs and Administration
Erya Ouyang, Rutgers Business School, Department of Marketing

This project examines the growing “attention–action gap” in nonprofit social media campaigns, where high engagement (likes, views, shares) often fails to translate into meaningful real-world outcomes such as donations, volunteering, or advocacy. The research will develop a multimodal framework to measure how specific combinations of images, text, and other content features shape both online engagement and offline behavioral impact. Using large-scale data integration across nonprofit registries, Meta fundraising content, and real-world behavioral indicators (including donation and location-visit proxies), the project will apply causal machine learning methods to identify when and why attention converts into action. The significance lies in providing nonprofits, platforms, and policymakers with evidence-based tools to evaluate and improve digital fundraising effectiveness beyond superficial engagement metrics. A key deliverable is the development of a domain-specific vision–language model capable of generating and optimizing nonprofit social media content to reduce the attention–action gap, positioning the project for NSF and applied fundraising research funding.