Rutgers University Camden

NSF Awards · FY 2026 · 2026-07

Metal-ion batteries are widely used in electric vehicles, portable electronics, and energy storage for the power grid. Lithium-ion batteries have enabled many of these advances, but they are reaching limits in energy density and cost. Calcium-ion solid-state batteries are a promising alternative because calcium is abundant, low-cost, and capable of storing large amounts of energy. However, progress has been limited by the lack of solid electrolytes that can efficiently transport calcium ions at room temperature and remain stable when in contact with battery electrodes. This project aims to speed up the discovery of high-performance calcium solid electrolytes and to improve understanding of how calcium ions move through these materials. It will use data-driven approaches and advanced computer simulations. Large materials databases will be screened to find promising candidates. First-principles simulations will be used to study ion transport at the atomic scale. The project will also support education and outreach by involving students in research and promoting STEM learning in local schools. These efforts will help prepare a skilled workforce. Overall, this project will help develop safer, lower-cost, and high-performance energy storage systems, thereby strengthening energy security in the United States. This project will focus on identifying, characterizing, and optimizing inorganic calcium-ion conductors that can approach superionic conductivity at room temperature. A key challenge is that most known materials either exhibit low Ca-ion mobility or undergo interfacial reactions that form interphases that block ion transport. Addressing this challenge will require both the discovery of new materials and a deeper understanding of the mechanisms governing ion diffusion and interfacial stability. To achieve these goals, the project will integrate data-driven materials discovery with high-throughput computational approaches. First, large-scale materials databases will be screened using data mining and bond-valence models to identify candidate calcium-ion conductors with favorable structural and chemical features. Second, calculations based on density functional theory will be employed to evaluate migration pathways, activation energies, defect formation, and electrochemical stability windows. Molecular dynamics simulations will further capture ion transport behavior at finite temperatures and provide insight into diffusion mechanisms and possible ion-correlation effects. Building on these insights, the project will explore strategies to enhance ionic conductivity through in silico doping and defect engineering. This includes introducing aliovalent dopants, tuning vacancy and interstitial defect populations, and optimizing lattice frameworks to reduce migration barriers. In parallel, the electrochemical and interfacial stability of candidate materials will be systematically assessed against calcium metal anode and relevant cathode materials to ensure compatibility. The outcomes of this project will include identifying new calcium superionic conductors, developing a fundamental understanding of Ca-ion transport mechanisms, and establishing design principles that provide actionable guidance for experimental synthesis and validation. 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.

NSF Awards · FY 2025 · 2025-09

Gram-negative bacteria are enclosed by two membranes with different compositions. The outer membrane normally has LPS, a lipid with sugars attached to it. We recently discovered that a model bacterium can survive without LPS if it produces CPG2, a type of ceramide lipid. This shows the importance of ceramides in bacterial biology and provides an opportunity to study alternative modes of outer membrane construction in bacteria. The proposed research is anticipated to discover how CPG2 is made and explain how the enzymes that make it work. This knowledge will be essential to discover the numerous roles of bacterial lipids in the various environments in which bacteria live, including the human body. Because many types of bacteria produce ceramides, this advance will contribute to answering important basic and translational research questions related to the production and function of lipids in all bacteria, including those causing disease. More broadly, this project will train students at the high school, undergraduate, and graduate levels. Specifically, these students will be trained in a multidisciplinary setting to develop skills in genetics, biochemistry, and structural biology. These skills are essential to develop the new generation of researchers, and a strong biotech workforce. Gram-negative bacteria are characterized by an envelope consisting of two membranes. The inner membrane is largely composed of phospholipids and functions similarly to the eukaryotic plasma membrane. By contrast, the bacterial outer membrane is unique in that it is asymmetric with an outer leaflet comprised mainly of the glycolipid lipopolysaccharide (LPS) which creates a permeability barrier against hydrophobic molecules including many antibiotics. Given its critical role in Gram-negative bacterial physiology, the genes involved in LPS synthesis are generally essential. Recently, our lab has characterized a mutant strain of Caulobacter crescentus that survives without LPS by synthesizing a novel anionic sphingolipid with a diphosphoglycerate headgroup (CPG2). Genetic analyses identified at least four genes required for CPG2 synthesis, but their specific enzymatic functions remain unclear. We have previously demonstrated that the first enzyme in this pathway is a ceramide kinase (CERK), distinct from the eukaryotic CERK. The remaining enzymes in this pathway have little homology to known lipid-modifying enzymes and provide a platform for uncovering new enzymatic activities and mechanisms. Our central hypotheses are that 1) proteins CCNA_01225 and CCNA_01219 are responsible for adding the first glycerate to ceramide 1-phosphate (C1P) to form ceramide-phosphoglycerate (CPG) and 2) proteins CCNA_01210 and CCNA_01217 add the second phosphoglycerate to form ceramide diphosphoglycerate (CPG2). The broader impacts of this project relate to the training of a new generation of STEM researchers for the biotech workforce, at the high school, undergraduate, and graduate levels. Our multidisciplinary team will provide training in bacterial genetics, biochemistry, and structural biology. 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.

NSF Awards · FY 2024 · 2024-08

The growing dependence of organizations on cloud cyberinfrastructure (CI), coupled with the intrinsic on-demand and elastic nature of the cloud CI, have widened the attack surface and made it an attractive target to rapidly evolving cyber threats. The development of fairness-aware Artificial Intelligence (AI) and machine learning (ML) based security solutions can make cloud CI more resilient and trustworthy. However, a key pillar of a successful secure cloud adoption necessitates scientific research workforce training. This project aims to train the future research workforce to develop and use AI-based cloud CI cybersecurity solutions that are fair, ethical, and unbiased. In addition, the project aims to instill the ability of the workforce to adapt and evolve these AI based cybersecurity solutions for cloud CI to improve their trustworthiness and resiliency, as new adversary models are discovered. The technical innovations of this project address the growing needs for a fairness-aware AI-skilled secure cloud CI research workforce in two-fold. First, the project will develop and integrate seven advanced experiential learning modules, referred to as AI4SecureCI, for secure cloud CI using fair and explainable AI concepts into undergraduate and graduate curriculum, training around 500 diverse participants including faculty and students directly. The developed AI4SecureCI modules will include the concepts of network security, authorization and automated access control, online malware detection, classifying malware threats, adversarial attacks and defenses, bias and fairness, and explainable AI, relevant to cloud CI. These modules will include the (1) lecture materials to provide conceptual knowledge for AI4SecureCI, and (2) hands-on lab exercises to provide practical experience. To support hands-on labs and enable wider adoption of the modules, the team will utilize ready-to-use datasets created from their own cloud CI security research and public security datasets, and free-tier cloud services such as AWS Educate. Second, the project will ensure broader adoption, via student boot-camps and series of faculty workshops of developed advanced AI4SecureCI and computational data-driven methods, into underrepresented groups of CI users and contributors to foster research advancements for evolving cloud CI security threat vectors. The advances made under this project, both in terms of research, modules developed, as well as training material will be made publicly available on a project website. The team will collaborate closely with the NSF ACCESS program to enhance the dissemination of knowledge and expertise within the CI community by incorporating the AI4SecureCI modules into the ACCESS Knowledge Base. "" 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.