New Jersey Institute Of Technology

NSF Awards · FY 2026 · 2026-09

Modern digital platforms increasingly rely on automated decision systems to allocate resources and opportunities in real time. Examples include matching riders with drivers in ride-hailing services, assigning tasks to workers in online labor markets, allocating advertisements on search platforms, and selecting offers in digital marketplaces. These systems must make immediate decisions under uncertainty, often without knowing future demand, arrivals, or market conditions. While decades of research have produced powerful algorithms for such problems, most theoretical analyses rely on unrealistic assumptions that rarely hold in practice, such as unlimited connectivity between participants or extreme variability in economic values. In real systems, decisions are constrained by geography, market design, and predictable patterns of user behavior. This project develops new algorithmic principles for reliable online decision-making that explicitly account for these realistic constraints. The results aim to improve the stability, transparency, and reliability of automated decision systems used in digital platforms and resource allocation systems. The project establishes a theoretical framework for parameterized robustness in online decision algorithm. The work studies how realistic structural parameters, including bounded connectivity, constrained value ranges, and predictable variance, affect algorithm performance traditionally analyzed only under worst-case assumptions. The project develops parameter-dependent analyses that characterize algorithm guarantees as functions of these parameters, introduces a smoothed competitiveness framework that evaluates algorithms under structured stochastic environments, and studies algorithms that incorporate machine learning predictions while maintaining robustness when predictions are inaccurate. In addition, variance-based analysis will quantify the stability of algorithmic outcomes beyond expected performance. Together, these directions advance the theoretical foundations of reliable decision-making algorithms for large-scale digital systems operating under uncertainty. 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 2026 · 2026-07

Cancer becomes life-threatening when tumor cells spread from the original tumor to other parts of the body. This spreading process depends not only on the cancer cells themselves, but also on the surrounding tissue called the extracellular matrix (ECM). The ECM provides physical support to cells. The structure and mechanical properties of ECM change with age and with diseases such as diabetes. These changes in ECM may make it easier for tumor cells to spread into healthy tissue, but the underlying mechanisms are not well understood. This project will study how aging- and diabetes-related changes in the ECM affect cancer cell invasion in breast cancer. The research will combine laboratory experiments with mathematical modeling to predict condition under which cancer cells are most likely to spread and metastasize. The project will also include education and outreach activities that will have broad societal impact. The project will train K-12 to graduate students in interdisciplinary research at the intersection of engineering and cancer biology. This project will establish a quantitative, energy-based framework for predicting tumor invasion as a function of ECM physical properties, with a particular emphasis on changes associated with aging and diabetes. Aging and diabetes alter key features of the ECM, including collagen concentration, crosslinking, stiffness, pore size, and structural anisotropy, creating complex mechanical environments that regulate cell behavior. However, existing studies report conflicting conclusions regarding how ECM mechanics influence cancer invasion, underscoring the need for a unifying physical framework. The project will integrate controlled experiments, thermodynamic modeling, and computational analysis to quantify the energetic barriers that govern whether cancer cells invade the surrounding matrix. By measuring how ECM physical properties regulate cellular forces, shape changes, and cell-matrix interactions, the research will compute the total free energy of the tumor-ECM system and use it to predict invasion likelihood. These predictions will be tested using 3D bioprinted tumor spheroid platforms that allow independent control of ECM stiffness, pore size, composition, alignment, degradability, and the ability of cells to mechanically remodel their surroundings. The expected outcomes include a predictive framework linking ECM physical state to cancer invasion, mechanistic insight into how aging and diabetes alter tumor progression, and experimentally validated models of pathological tissue environments. Together, these results will advance fundamental understanding of cancer mechanobiology while laying a quantitative foundation for future strategies that target the tumor microenvironment. 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.

NIH Research Projects · FY 2026 · 2026-06

PROJECT SUMMARY/ABSTRACT Heart disease is the leading cause of death worldwide. Leadless intracardiac pacemakers, an alternative to traditional pacemakers, are effective for treating heart block and ventricular dysrhythmias, but their utility is limited by short battery life, with over 40% failing within three years. This is problematic as pacemaker recipients now live an average of over 15 years post-implantation, representing a significant mismatch that both clinical practice and quality of life issues. Studies have highlighted various complications, risks, and costs associated with leadless pacemaker implantation, underscoring the urgent need for alternative power solutions for implantable cardiovascular devices to improve patient care. However, existing alternative power solutions, burdened with complications and potential risks, and requiring additional thoracotomies for suturing cardiac energy harvesting (EH) devices onto the heart's surface, are not clinically acceptable. A self-sustainable, clinical translational energy strategy is therefore critically needed to improve the longevity of leadless intracardiac pacemakers. The objective of this project is to develop an innovative clinical translational strategy for cardiac energy harvesting. This involves designing advanced nanomaterials and bistable structures to seamlessly integrate with current leadless pacemaker technology, utilizing unused spaces of pacemaker to convert heartbeat vibrational motions into tens of micro-watts of power output to recharge pacemaker batteries. Our hypothesis is built upon the identification that a leadless pacemaker can maintain the intimate interaction along the heart’s diastole and systole. Thus, integrating piezoelectric cardiac EH devices with a leadless pacemaker is expected to transfer the cardiac motions within right ventricle into the strain of the piezoelectric material and in turn generate electrical power to extend the battery life of pacemakers. In our specific aims, firstly, we will engineer nanostructures with high compressibility, designing novel piezoelectric porous polymers for effective mechanical-electrical conversion. Secondly, we will develop geometrical mechanics designs to leverage heart motions and integrate them with existing leadless intracardiac pacemaker technology. Thirdly, we will systematically design and perform experimental evaluations, considering complex parameters in clinical implementations such as anchor positions, heartbeat effects, and blood flow fluctuations, and test the energy harvesting performance in swine models for clinical validation. The overall approach is ideal for clinical translation since it is compatible with the existing leadless pacemaker implantation techniques and does not require thoracotomy for suturing of cardiac energy harvesters. The proposed project is high risk-high impact, and the research findings will advance knowledge of engineering fundamentals as they apply to biomedical and materials sciences, which will enable significant new opportunities for basic and translational health studies.

NSF Awards · FY 2026 · 2026-05

Large Language Model (LLM)-assisted coding, where an LLM automatically generates code based on a developer-specified prompt, is already popular and projected to grow, promising to bolster coding productivity while reducing software development time and effort. LLM-generated code can be insecure for a variety of reasons, for example omitting critical security checks, or containing mistakes that adversaries can exploit. When this insecure code eludes scrutiny and makes its way into production systems, our software infrastructure is at risk. This project advances the state of research and practice in LLM-assisted coding by using program analysis and verification to generate secure code. The project's novelties are to introduce and use guardrails named contexts that define security properties and guide LLMs into producing code that meets such security guarantees. The project's broader significance and importance are empowering programmers to understand the security implications of LLM-assisted coding and in turn write more secure code efficiently, helping fuel economic prosperity and increasing national security. The project consists of three thrusts. The first thrust uses program analysis to bridge the gap between security properties and LLM code generation via contexts and LLM prompts. The second thrust constructs an iterative LLM-centered code generation approach with criteria designed to improve security in each iteration. The third thrust develops a minimization approach, designed to produce minimal code examples in situations where LLM generation fails, or the LLM cannot converge toward producing secure code. These approaches are usable in a variety of other settings: scenarios where LLM-generated code needs to be rigorously or formally verified, settings where the LLM-produced code must meet certain specifications from the start, and the widely-applicable technique of generating a minimal example when LLM generation fails, so the programmers can easily understand and mitigate code generation failures. 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.

NIH Research Projects · FY 2026 · 2026-05

The major health significance of this proposal is the delivery and local release of therapeutic gases from echogenic microbubbles using ultrasound as a novel treatment for neonatal hypoxic-ischemic (HI) encephalopathy. Hypoxic-ischemic encephalopathy (HIE) is a major cause of mortality and long-term neurological disabilities in children including cerebral palsy, seizure and behavioral disorders. Remarkably, there are no clinically approved drugs to directly treat HIE, with therapeutic hypothermia being the only major approved treatment. In recent years, noble gases like xenon (Xe) and argon (Ar) have shown great promise as cytoprotective agents that can treat HI injuries via inhalation. Whereas Xe has been researched in greater detail, including in early clinical trials, Ar treatment has been limited to animal studies. Xe is considered more effective in its treatment, while Ar is a hundred times cheaper and more widely available, while still providing excellent cytoprotective efficacy. The mechanism of Xe action depends on its interaction with glutamate receptors on cell membranes, while Ar is thought to stimulate various intracellular protection signaling pathways, independent of glutamate receptor binding. Therefore, we aim to test the hypothesis that synergistic treatment with both Xe and Ar will be a versatile and highly effective therapeutic platform. Importantly, both Xe and Ar can freely diffuse through the blood brain barrier (BBB), overcoming a major hurdle for current HIE candidate drugs. However, currently Xe and Ar are delivered systemically, (thus non-targeted) which for xenon, is highly expensive. As a solution, we propose to use microbubbles (MBs) containing pure Xe or Ar that can be ruptured via clinical ultrasound to release the gas into the cerebral arteries where it can traverse the BBB to reduce secondary injury. MBs are inherently echogenic due to the non-linear oscillations induced by clinical ultrasound. The PI has previously developed MBs containing hard-to-stabilize noble gases that are echogenic in vivo. These MBs have successfully treated other forms of brain injury. The PI has also shown that Xe and Ar can reduce damage to the BBB itself post-injury. Based on these prior studies, we hypothesize that both individual and combination therapy with Xe MBs and Ar MBs will be highly effective in treating neonatal HI, as measured using an in vitro HI model with human neurons and glia as well as using a highly translational in vivo model with histological and behavioral readouts. The proposed research has the following specific aims. (1) Test the hypothesis that Xe and Ar MBs will reduce the extent of injury when delivered a) after HI in rats alone or in combination with therapeutic hypothermia, and b) after oxygen glucose deprivation of human cells in vitro; (2) Test the hypothesis that administering Xe and Ar MBs alone or in combination with therapeutic hypothermia after HI will provide long- term preservation of neurological function. Successful completion of these objectives will lead to a pioneering new local delivery agent (MBs) with no adverse side-effects, that penetrates the BBB for image-guided treatment of neonatal HIE using a relatively inexpensive and non-invasive clinical ultrasound technique.

NSF Awards · FY 2026 · 2026-05

This project requests support for U.S.-based doctoral students to attend the SIGMOD 2026 (ACM Special Interest Group on Management of Data) conference. The annual ACM SIGMOD Conference is a leading international forum for database researchers, practitioners, developers, and users to explore cutting-edge ideas and results, and to exchange techniques, tools, and experiences. This proposal would enable 15 US-based students to attend the conference. This travel support will strengthen the national artificial and information technology workforce pipeline by providing intensive, mentored professional development for doctoral researchers who will become the next generation of faculty, research and development scientists, and innovation leaders. The conference's focused training in problem framing, evaluation, and communication across disciplines increases participants' capacity to conduct high-impact interdisciplinary research. This travel support will also lower barriers for participation by students in STEM, based on merit, strengthening the AI research and learning community. 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 2026 · 2026-04

This award will support 20 U.S. graduate students' registration for the Dissertation Research Roundtable at the 2026 ACM Computer-Human Interaction (CHI) conference on Human Factors in Computing Systems. CHI has for years been the leading international forum for the presentation and discussion of research and practice related to human-computer interaction; since 1986, the conference has organized professional development opportunities for graduate students that have launched the careers of many outstanding HCI researchers. This year's event, the Dissertation Research Roundtable, is a research-focused workshop where a panel of distinguished research faculty provides research and career guidance for a group of selected promising PhD students. The event promotes rigorous intellectual exchange around dissertation-stage work, including research framing, methodological choices, ethical considerations, and theoretical integration. Structured discussion among thematically clustered students and experienced mentors fosters cross-project learning and improves the quality and impact of emerging research, including in areas such as human-artificial intelligence teaming and interaction. By prioritizing participation from students at under-resourced institutions and across a wide range of HCI subfields, the project also broadens the intellectual perspectives represented in the community and contributes to the long-term advancement of the field. Beyond the intellectual contributions, students receive valuable opportunities to build their professional networks with each other, with the expert panel, and (through open poster sessions presenting their research) the larger community. Criteria for selection include appropriate timing in the student's career and dissertation project, the value the student will both receive and bring to the consortium, and the need for travel funding. In alignment with the consortium's overall goal to increase the range of the CHI community, the selection committee will also seek to fund students from a wide range of personal, professional, disciplinary, and institutional backgrounds. In particular, the selection committee will emphasize providing opportunities for students from institutions that have relatively few institutional and faculty resources devoted to Human-Computer Interaction, widening the range of institutions and students that participate in the field and helping to build capacity at those institutions. 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.

NIH Research Projects · FY 2026 · 2026-03

Peripheral nerve injury (PNI) remains highly pervasive in modern society with over twenty million recorded cases in the United States alone. These injuries often result from traumatic injury, such as those presenting from car crashes, falls, or electric shock. The gold standard for PNI is an autologous nerve transplantation, which is limited by the generation of an additional surgical site, donor-site morbidity, and neuroma formation at the site of harvest. Our long-term goal is to develop tissue systems that will allow for the study of axonal growth and regeneration to ultimately develop clinically relevant strategies to stimulate targeted repair of longer peripheral nerve injuries. The overall objective of this proposal is to construct 3D systems to study axonal growth and regeneration. We hypothesize that finely controlled spatial and biochemical cues can significantly enhance axonal growth and regrowth in a 3D model to screen potential drug candidates for peripheral nerve regeneration. To systematically test this hypothesis, the following specific aims are proposed: Under the first aim, embryonic dorsal root ganglia (DRG) explants will be seeded in 3D hollow channels coated with specific ECM constituents located in the native axonal niche with or without Schwann cells (SCs) to evaluate their ability to support enhanced axonal growth and SC migration in real-time. Under the second aim, DRG explants will be cultured in medium supplemented with various drug compounds to develop a pre-clinical drug discovery model, which we will evaluate with certain non-steroidal anti-inflammatory drugs (NSAIDs) that may improve axonal growth/regrowth and enhance calcium signaling. Under the third aim, we will determine the mechanism of action for targeted NSAIDs using appropriate gain-of-function and loss-of-function experiments, confirming that these effects are independent of NSAID- mediated regulation of COX-1/2. Each Aim will be developed with embryonic DRGs, and validated with adult DRGs to maximize translatability. The approach is innovative, in our opinion, because it represents a substantive departure from the status quo by correlating axonal outgrowth rate and direction with calcium imaging techniques to quantify neuronal activity. Further, we will develop a model system that can be used to screen drugs, as there are no drugs currently administered to specifically treat peripheral nerve repair. The approach is significant because there is a clear need for effective systems to predict drug performance prior to pre-clinical or clinical trials to streamline the drug development process. The proposed model system will be able to separate out the study of physical cues, such as ECM molecules and integrin signaling, with soluble cues. The outcomes of this proposal will be the development of an architecturally relevant 3D tissue model to study axonal growth and repair that can screen potential drugs prior to pre-clinical studies to increase success rates and reduce development time and cost. The findings resulting from the proposed studies will enable the development of future biomaterial- based treatments to provide a translational pathway for these treatments towards the clinic to improve patient quality of life.

NSF Awards · FY 2025 · 2025-10

Emerging wearable technologies promise transformative capabilities in personal health, environmental monitoring, and smart textiles. However, current wearable systems often rely on rigid components, adhesives, or external batteries that limit comfort, adaptability, and long-term usability. These limitations create significant barriers to continuous, real-world use, hindering progress toward truly seamless integration of electronics with the human body. To address these challenges, this project introduces a new class of flexible nanofibrous devices that are lightweight, breathable, self-powered, and capable of converting body motion into usable energy while simultaneously performing on-body sensing. By leveraging advanced materials science and innovative fabrication techniques, these nanofibrous devices eliminate the dependence on bulky, rigid components, significantly enhancing user comfort and enabling long-term use. The resulting technologies have broad potential applications spanning fitness, rehabilitation, environmental sensing, and consumer wearables, where comfort, durability, and reliability are critical. Beyond technological innovation, this project also contributes substantially to STEM education by providing multidisciplinary research opportunities for students, integrating new course modules into the NJIT engineering curriculum, and engaging with K–12 outreach programs designed to inspire and broaden participation in STEM fields. This project focuses on designing and fabricating flexible, breathable, and biocompatible nanofibrous devices for next-generation flexible electronics. The primary goals include developing advanced porous nanomaterials with enhanced mechanical-to-electrical energy conversion capabilities, establishing versatile and scalable fabrication methods, such as electrospinning combined with dynamic phase separation, to precisely control nanostructure formation within individual fibers, and demonstrating effective self-powered biomechanical energy harvesting from various body locations to enable comfortable, integrated sensing devices. These devices are designed to harvest biomechanical energy generated by natural body movements, such as walking, joint articulation, and other common motions, converting it into electrical energy to power embedded sensors without reliance on batteries. The intellectual merit of this work lies in its interdisciplinary approach, advancing foundational knowledge at the intersection of materials science, nanomanufacturing, and bioelectronic device integration. By bridging these fields, the project establishes a foundation for the development of adaptive, sustainable, and multifunctional wearable systems. Furthermore, it introduces a versatile platform technology with wide-ranging applications in health monitoring, smart textiles, environmental sensing, and human-machine interfaces. Complementing the technical research, the project incorporates comprehensive educational initiatives including new curriculum development, multidisciplinary student training, and outreach efforts, fostering innovation and broadening participation in STEM fields. These combined research and education activities strengthen both technological advancement and societal impact, ensuring that the benefits of flexible nanofibrous electronics extend beyond the laboratory to positively influence health, industry, and workforce development. 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 · 2025-10

With support from the Improving Undergraduate STEM Education: Hispanic-Serving Institutions (HSI Program), this Level 1 Implementation and Evaluation project at New Jersey Institute of Technology aims to provide critical skills to undergraduate students in engineering technology. The project will integrate entrepreneurial education modules into MET103: Graphics and Computer Aided Design, a first-year course taken by all engineering technology students. An entrepreneurial mindset and skill set will allow students to bring a translational perspective to their degree program and entry into the STEM workforce. Students will be engaged to co-develop the project modules, which will strengthen the connection of the course materials to students' authentic perspectives and interests. Modules will be revised and improved over several iterations as part of their integration into MET103. Building student understanding of the challenges and opportunities in developing a business has potential to magnify the creativity of the next generation of STEM professionals. The goals of this project are: (1) examine engineering technology students' perspectives/experiences regarding entrepreneurial motivation; (2) co-develop entrepreneurial modules with students; (3) iteratively integrate modules into MET103; and (4) conduct a mixed methods study of student attitudes before and after utilizing the project modules in MET103. The project will investigate the impact of the modules on the entrepreneurial mindset and self-efficacy of participating students. Data streams include surveys and semi-structured focus groups with a "think-aloud" aspect to capture more complicated student thinking. The project will be evaluated by an independent evaluator to assess progress towards stated goals. This project is funded by the HSI Program, which aims to enhance undergraduate STEM education, broaden participation in STEM, and increase capacity to engage in the development and implementation of innovations to improve STEM teaching and learning at HSIs. 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 · 2025-10

Non-technical Description: A central goal of Materials Genome Initiative is to predict superior intrinsic material properties on the atomic scale and translate them to superior technologies we can touch and hold. However, most such material discoveries are lost in translation due to the “mesoscale cliff.” Materials promising on the nanoscale may fail in devices where microstructures up to hundreds of micrometer in size dominate device performance. This team addresses this materials challenge to develop a fundamental knowledge base to deploy the next generation of cryogenic electro-optic materials integrated on silicon for chip-scale quantum integrated circuits. The electro-optic effect describes a material’s optical refractive index change upon the application of an electric field. Electro-modulators power our internet today by converting electrical to optical signals. They are also fundamental building blocks for the emerging scalable optical quantum computing hardware, on-chip trapped ion quantum computing schemes and developments in low temperature science. With these, new materials’ challenges arise, in that, electro-optic modulators must now respond in the gigahertz frequency range, be operated at cryogenic temperatures with low energy budget and must be integrated directly on silicon. This requires materials with cryogenic electro-optic coefficients that are many orders of magnitude higher than the current industry standard. In addition, they require large index and low optical loss at the telecom wavelength with low microwave dielectric constant and loss for low power, low loss operation. A lack of fundamental understanding on the mesoscale is undermining this effort with severely degraded performances in going from atoms to devices. This research team aims to make an impact here with a new theory approach informing experimental breakthroughs. Technical Description: The research team plans an Atoms-to-Devices design approach that is firmly rooted in the materials genome framework. It has three interlocking thrusts: (1) Density Functional Theory informed Thermodynamic Theory of Electro-Optics builds the foundational bridge between the atomic scale and the mesoscale using a modern thermodynamic theory of electro-optics to predict and validate new materials with superior intrinsic electro-optic properties. (2) Thermodynamics integrated Phase-Field Simulation implements the thermodynamic electro-optic theory using phase-field modeling to predict and experimentally validate complex mesoscale microstructures and their effective electro-optic properties. (3) An Open-source Phase-Field-integrated Electrodynamics simulation software package integrates phase-field modeling and electrodynamics simulations to design a digital twin of the physical modulator devices and their performances. A robust experimental testing and validation is built into each Thrust. Graduate and undergraduate students along with postdoctoral researchers and principal investigators will train together in an iterative closed-loop materials design crucible. Undergraduate, graduate and post-doctoral mentoring of personnel will ensure a robust pipeline for the next generation of workforce in quantum science. A website will be developed that will serve as a medium for disseminating the team’s work. Research breakthroughs with potential for technology translation potential will be communicated through Industry outreach that would also benefit student career development. 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

This I-Corps project focuses on commercializing a portable, cost-effective, and highly accurate sensor for detecting specific airborne chemical indicators associated with health and agricultural issues. Many critical human health conditions and plant diseases produce unique airborne chemical signatures, known as volatile organic compounds. Current diagnostic approaches rely on complex, expensive, and time-consuming laboratory analyses, requiring samples to be sent off-site. Transportation delays slow intervention and increase costs, hindering widespread early detection. For instance, the early detection of certain human diseases, such as lung cancer, dramatically improves survival rates; however, traditional methods are not readily accessible. Similarly, a substantial portion of global food production is lost annually due to plant diseases, threatening food security and economic stability worldwide. Effective, on-site detection tools are urgently needed to mitigate these challenges. This sensor technology enables rapid and precise identification of these chemical indicators directly where they are required – whether in a clinic or on a farm. Its compact design eliminates the need for cumbersome sample collection and transport, providing immediate results. This I-Corps project utilizes experiential learning, coupled with a firsthand investigation of the industry ecosystem, to assess the translation potential of the technology. This solution is based on the development of a portable, integrated sensor platform designed for the sensitive and selective detection of airborne volatile organic compounds. The platform incorporates a novel microfluidic electrochemical sensor that facilitates direct, in situ, gas-liquid interactions within a compact architecture. Embedded microelectrodes enable simultaneous electrochemical measurements, enhancing the signal-to-noise ratio. This sophisticated microfluidic design significantly improves the mass transport of target analytes to the sensing interface, resulting in superior sensitivity compared to conventional methods. The system integrates a fluidics module for precise gas sample delivery and a detection module capable of executing various electrochemical techniques, including electrochemical impedance spectroscopy, cyclic voltammetry, and differential pulse voltammetry. A data analysis module processes the electrochemical signatures using trained machine learning models to provide accurate qualitative and quantitative predictions of the compounds present. This integrated approach represents a significant scientific advancement over bulky and time-consuming laboratory-based techniques, offering a robust, rapid, and cost-effective alternative for on-site diagnostics. The goal is to provide immediate, actionable insights for diverse applications, enabling faster decision-making and intervention, thereby transforming current diagnostic workflows in human health and agriculture. 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

This I-Corps project is based on the development of a home aquaponic system used to grow high-value fish species and organic plants in a compact, sustainable design. Aquaponics combines growing fish and growing plants without soil in a closed loop system. The technology developed for this system uses crushed, demolished concrete in the filtration medium, to improve the environment for beneficial bacteria that convert fish waste into nutrients for plants. Beneficial bacteria are key in converting fish waste (ammonia) into materials that plants can absorb. Repurposing concrete rubble turns waste into a productive component of a closed-loop system. In addition, this technology is designed to use less water than traditional soil gardening and may offer a sustainable and organic approach, eliminating the need for synthetic fertilizers while reducing waste. Currently, there is a rising demand for at-home food production systems that promote food independence. This technology may allow users in urban areas to grow organic produce and fish at home. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of an integrated aquaponic system that leverages recycled, demolished concrete as a functional component of its biological filtration system. The concrete acts as a pH stabilizer, enhancing the performance of nitrifying bacteria that convert ammonia into plant-friendly nutrients. This technology offers dual environmental benefits, purifying water within the system while diverting construction debris from landfills or waterways. In addition, the system is designed to include multilevel hydroponic plant cultivation, energy-efficient fish feeding mechanisms, and low-maintenance design for consistent performance. Compared with current aquaponics systems, this approach offers a higher-efficiency, modular setup tailored for residential and small-scale commercial use. Users may benefit from increased food self-reliance, improved water quality, reduced system maintenance, and a more sustainable food source, while the environment may benefit from decreased construction waste. 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

About 12% of U.S. adults have difficulty with mobility, including walking or climbing stairs. Current technologies for helping people manage these issues are often expensive, heavy, and hard to use. The goal of this research is to develop a new paradigm for assistive robotics that will make it possible to take such systems from the laboratory to widely accessible tools for people. The research team will combine advances in robotic exoskeletons for human joints, simulation enabled by artificial intelligence, and other approaches to design lightweight, wearable robotic systems that can be personally controlled. The investigators will also test the systems in different settings to improve their usability. The project will develop new science and technology that have the potential to help people to perform daily activities, along with their quality of life and overall well-being. The project will design and test new assistive robotic systems by integrating artificial intelligence, robotics, biosensors, rehabilitation medicine, gerontology, and neurorehabilitation. The research will use a modular design approach, that can adapt to individual needs and daily activities without extensive calibration. By leveraging computational modeling and physics-informed deep reinforcement learning, the systems will learn control strategies from computer simulations and user feedback to deliver personalized support that addresses an individual’s mobility challenges and needs. The objective is to broaden the reach of robotic mobility support to a much larger population in a wide variety of settings. The resulting enhanced mobility and function can lead to broader benefits, including promoting independent living, employment, and well-being. 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

With the support of the Chemical Measurement and Imaging Program in the Division of Chemistry, Professor Hao Chen of the New Jersey Institute of Technology focuses on the development of a new mass spectrometric approach for detecting elusive intermediates involved in the degradation of per- and poly-fluoroalkyl substances (PFASs). PFASs, widely regarded as "forever chemicals", are extremely stable due to their high carbon-fluorine (C-F) bond energies. From a human health perspective, PFAS exposure has been linked to the generation of reactive oxygen species (ROS), oxidative stress, DNA damage, and an elevated risk of chronic conditions such as cancer and inflammatory disorders. The widespread presence and harmful effects of PFASs in the environment have made their degradation a critical research focus. However, PFAS degradation mechanisms are not fully understood, largely due to lack of information about their elusive degradation intermediates. The project aims to tackle this very challenging problem. Its success would greatly benefit the public by enabling researchers to understand PFAS degradation chemistry and to discover new and effective ways for PFAS removal. This project will engage undergraduates in the research and reach out to even younger students to acquaint them with this important interdisciplinary science, technology, engineering and mathematics (STEM) area. The difficulty in detecting and characterizing the transient PFAS degradation intermediates is due to their short lifetimes and low abundances. This research project will develop a novel in-situ high-resolution mass spectrometry (HRMS) approach to capture and detect short-lived degradation intermediates generated from either thermal or electrochemical degradation of PFAS. This project is expected to have a high impact in the fields of both analytical chemistry and environmental science. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Water waves are ubiquitous in nature, occurring everywhere from oceans and lakes to swimming pools and bathtubs. Their study has led to important engineering applications, such as tsunami detection, wave energy harvesting, and coastal protection using breakwaters. Water waves are typically studied under the assumption that they are periodic (i.e., possessing a spatially repeating pattern) or that they decay to zero at infinity (i.e., no waves are coming from the far away). However, these assumptions are often too restrictive for exploring more complex and realistic phenomena. For instance, large bodies of water such as oceans are often covered with waves of different wavelengths, propagating in all directions over vast distances. Furthermore, these wavelengths can be incommensurate. To accurately model the interactions of water waves, the investigator studies them in a spatially quasi-periodic setting. This project will advance the modeling and simulation of water waves, with broad applications in ocean wave forecasting and shoreline protection. The project will also offer research and training opportunities for students. One of the main goals of the project is to develop accurate and stable numerical methods for studying spatially quasi-periodic water waves in both two and three dimensions. These methods will then be used to simulate such waves over flat or variable bottom topography. Traveling wave solutions will be computed, and their stability will be analyzed. In addition, the project will contribute to the study of periodic water waves. The quasi-periodic framework will enable the investigation of the nonlinear instability of classical periodic traveling waves under subharmonic perturbations, whose wavelengths are incommensurate with those of the original wave. Beyond the numerical aspect, the project also aims to advance the theoretical understanding of spatially quasi-periodic water waves and address the local well-posedness of these solutions. 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

Fluid-structure interaction is an important and rapidly evolving field in computational engineering, with its outcomes driving significant advancements in sectors such as aerospace, renewable energy, and healthcare technology. Existing computational methods for solving fluid-structure interaction problems face limitations in scalability, energy efficiency, and integration of multi-physics components, restricting their applicability in complex real-world scenarios. This CAREER project aims to overcome these barriers by developing a scalable, open-source platform based on cutting-edge computational methods. The research focuses on enhancing the accuracy and computational performance of particle methods, enabling experimentally validated models for complex fluid-structure interaction scenarios. By providing this platform as an open resource, the project supports broader scientific progress and empowers engineers to design safer, more efficient systems in critical infrastructure. Integrated education and outreach plans bring new courses and projects on computational science, drawing students from diverse backgrounds into STEM fields and equipping them with the necessary skills to address complex societal issues. This project aligns with the mission of the National Science Foundation to advance science, support economic growth, and build a more inclusive STEM workforce. The technical objective of this CAREER project is to develop a scalable, hardware-accelerated, open-source platform that leverages Smoothed Particle Hydrodynamics to model complex fluid-structure interaction problems. The project builds upon an existing code infrastructure by implementing and validating new physical models. This is followed by a parallelization phase that adapts the software architecture to support simulations on graphics processing units, both within a single node and across multiple nodes. The project includes a comprehensive experimental campaign investigating the complex three-dimensional problem of liquid sloshing in partially filled reservoirs. The experiments serve a dual purpose of generating benchmark data for software validation and offering critical insights into the dynamics of fluid behavior in aerospace and space systems. This project pushes the boundaries of fluid-structure interaction modeling by extending the use of Smoothed Particle Hydrodynamics to real-world, high-stakes scenarios. It identifies and addresses current limitations of meshless methods when applied to large-scale problems, offering tools that drive progress in areas such as renewable energy, environmental safety, and healthcare innovation. 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

In this CAREER project, funded by the Chemical Mechanism, Function, and Properties Program of the Chemistry Division, Professor Pier Alexandre Champagne of the Department of Chemistry and Environmental Science at the New Jersey Institute of Technology is employing a combination of experimental and computational techniques to advance the chemical understanding of reactive sulfur species, which are of significant importance in biochemistry. The goal of this research is to access new synthetic donors of reactive sulfur species, use the donors to probe the behavior of those species, and apply computational tools to obtain a holistic understanding of their chemical reactivity. Regarding educational activities, the Visualize Organic Chemistry online learning platform will be expanded with new animations and tutorials that support greater mechanistic literacy among organic chemistry students and practitioners across the United States. Reactive sulfur species are a wide class of biochemical intermediates that have important physiological effects but that are still not well understood due to their thermodynamic and kinetic instability. The proposed computations will address this issue by characterizing the intrinsic reactivity of polysulfides with biological nucleophiles and electrophiles using Density Functional Theory calculations, allowing a holistic understanding of their behavior in vivo. The proposed synthetic donors of reactive sulfur species will provide access to new photoactivated tools to study the chemical reactivity and biochemical effects of reactive sulfur species, by leveraging the BODIPY photocage scaffold. These will enable the study of structural effects on persulfide reactivity, and the experimental characterization of the chemical properties of hydrotrisulfides, heralding a new era in the study of polysulfides that are critical to biochemistry. 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

This project aims to understand the propulsion mechanism and interactions of vertically oscillating objects that float on a fluid interface. These newly discovered “surfers” have been shown to self-propel along a fluid bath, and to self-organize through their mutual wave field into moving clusters. However, the mechanisms by which they self-propel and interact have remained elusive. The insights gained from this research have the potential to impact the biology and engineering communities, as scientific attention has been given to the self-propulsion of both inanimate objects and living organisms at the liquid-gas interface. These observations have inspired the development of biomimetic robots that self-propel using similar mechanisms. Capillary-scale robots have many engineering applications including environmental monitoring, water remediation and cargo transport. The project will train undergraduate and graduate students in mathematical modeling and physical applied mathematics, giving them tools that can be broadly applied to problems arising in the natural sciences and engineering. From a mathematical perspective, the main challenge is that surfers generate interfacial waves, unlike so-called “dry” active matter systems whose constituents propel and self-organize solely through steric interactions. A consequence of the inertial wave-mediated coupling between constituents is that multiple interaction modes coexist for the same experimental parameters. To address this challenge, a three-pronged approach will be utilized to develop mathematical models capable of successfully describing the surfers’ dynamics. The first goal is to develop a new mathematical and numerical framework for modeling the waves and flows generated by a single surfer, and the dependence of both on the surfer’s geometry. The mathematical challenge is to prove the existence of and derive approximations to solutions to a linear elliptic boundary value problem with mixed boundary conditions. Doing so will uncover the self-propulsion mechanism of surfers. The second goal is to simulate and analyze the collective behavior of surfers. Continuum partial differential equations (PDE) models will be systematically derived and completed using closure conditions, which will be benchmarked against particle-based models. The third goal is to develop new data-driven PDE discovery techniques to learn the governing mean-field equations for collectives of surfers, directly from experimental video data provided by collaborators. These results will be compared with the models based on physical principles developed throughout the grant period. This program will pave the way for the adoption of machine learning tools in understanding interfacial active systems. Generally, the surfer system represents a versatile and accessible platform for the exploration of active matter, yet contains mathematical modeling and simulation challenges due to the effects of fluid inertia and waves. 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

This project investigates Floquet materials—engineered systems whose properties are dynamically altered using time-periodic forcing, such as shining light on graphene or modulating optical waveguides. This approach allows for reversible control of physical behavior and has gained traction in fields like quantum engineering, photonics, and acoustics. Although experimental studies have observed intriguing phenomena like edge conduction in these materials, the theoretical foundation—especially in continuum models described by partial differential equations—is incomplete and, at times, contradictory. The investigator develops a rigorous mathematical framework for understanding Floquet materials using continuum models, with the goal of explaining fundamental features such as wave localization and energy transport. The project serves the national interest by advancing foundational science in applied mathematics and mathematical physics, and contributing to emerging technologies that rely on wave manipulation. The project supports graduate education at the New Jersey Institute of Technology and promotes collaboration and dissemination of scientific knowledge through scientific workshops and seminars. The investigator studies time-periodic parametric forcing in periodic media, focusing on non-autonomous dispersive partial differential equations (PDE) — specifically Schrodinger and Dirac equations—in contrast to the prevalent use of discrete, tight-binding approximations. The project addresses four core challenges: (1) developing the theory of “effective gaps” for bulk Floquet Hamiltonians in continuum settings; (2) analyzing the long-time behavior and radiation damping of edge modes; (3) establishing new dispersive decay estimates for Floquet Hamiltonians; and (4) exploring the potential for a PDE-based analog of the Floquet topological bulk-edge correspondence. The project integrates tools from spectral theory, asymptotic analysis, homogenization, and infinite-dimensional dynamical systems, offering novel perspectives on Floquet engineering and contributing broadly to the analysis of non-autonomous PDEs. 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

Solar flares are transient, yet dramatic energy release events in the Sun’s atmosphere that, together with the closely associated phenomenon of coronal mass ejections (CMEs), form a fundamental part of geo-effective space weather. Since their first discovery in 1859, substantial efforts have been made in understanding the energetics of flares. However there remains outstanding questions concerning the ways in which energy is released and transported in flares. We propose a comprehensive project targeted at advancing our understanding of flare energy deposition into the solar chromosphere. This project will focus on spectral lines of orthohelium, which have been shown to exhibit the unusual characteristic of initially dimming in the earliest stages of energy deposition into the lower atmosphere, before then brightening. These dimmings have been shown to be related to the properties of non-thermal particles present in flares. Using a combination of exceptionally high spatial resolution imaging spectroscopy of the He I 10830 Å and D3 5876 Å lines (via the 1.6m BBSO/GST) and state-of-the-art radiation hydrodynamic modelling (RADYN and RH codes) we will: 1) Take new observations using GST and perform analysis of the spatial and spectral characteristics of flare emission and enhanced absorption of the He I 10830 Å and D3 5876 Å, both individually and in relation to each other; and 2) Determine if our models are consistent with those observations, in terms of contrast level, the temporal behavior and spectral properties, and hence infer the physical conditions that produce the helium spectra. The team comprises both ground-based and space-based observational experts, and modelling experts, allowing us to complement the BBSO/GST observations with IRIS and SDO data and forward modelling. 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

Photonic and chiral photonic crystals—materials engineered with nanometric to micrometric periodic structures—are of great significance to science, industry, and national defense. Their ability to control and manipulate light enables various technological applications, including optical communication devices, lasers, sensors, solar cells, and thermal management systems. In materials science, some defects can be intentionally added to these periodic structures to create new materials with advanced properties, such as precisely controlling light in specific areas and accurately routing light signals. The ability to control waves in a small space can allow for more compact device designs. Studying how light interacts with periodic structures that contain defects will offer a cost-effective method for optical testing of designs—a crucial step in developing these new materials. This project will contribute to the study of inverse electromagnetic scattering theory in complex periodic media. The main objective is to simulate wave–material interactions and reconstruct defects using measurements of the scattered waves at a certain distance. This research also supports quality control of optical devices fabricated from purely periodic structures. Graduate and undergraduate students will participate in and receive training as part of this research. This project investigates the direct and inverse scattering problems governed by Maxwell's equations in an infinite locally perturbed bi-periodic layer, coupled with general constitutive relations. In addition to the inherent complexity of Maxwell’s equations, the presence of defects breaks the periodicity, posing significant challenges for both theoretical analysis and numerical solvers. Various techniques—including the Floquet-Bloch transform, volume integral equations, the fast spectral method, qualitative methods, and other approaches—will be flexibly employed to develop useful tools for addressing the challenges in both problems, summarized in four major topics. Questions to be addressed for the direct problem include (i) establishing a Rellich-type identity to study the existence and uniqueness of solutions of Maxwell's equations and (ii) developing a novel numerical method that combines the Floquet-Bloch transform, volume integral equations, and a fast spectral method to solve the problem and study the convergence. Questions to be addressed for the inverse problem include (iii) designing an innovative inversion scheme to reconstruct defects using the measurements of the scattered waves, without relying on prior knowledge of the periodic structure and requiring only minimal data for accurate performance and (iv) investigating the discreteness of interior transmission eigenvalues for such scattering media. 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

Stochastic ordinary differential equations on random graphs provide a general framework for modelling a range of important applications, including electrical power grids, communication or social networks, machine learning algorithms, epidemiology disease spread, neuroscience, and material models in statistical physics. This project will develop new characterizations of how interactions among large networks of individual particles or agents lead to important larger scale macroscopic phenomena. Examples range from the billions of neurons in the brain that can form macroscopic oscillations that are observable through electrocardiograms (ECG), to individual populations that may incur large outbreaks in disease spread. The project will formulate new classes of macroscopic models that incorporate the microscopic effects of network delays, and local connectivity. It will also involve the training of graduate and undergraduate students in this research area. This project aims to rigorously establish new macroscopic autonomous McKean-Vlasov equations (Fokker Planck type, hydrodynamic limits) for large systems of stochastically interacting particles on random graphs. A key mathematical contribution will be the introduction of new non-standard empirical measures that encode sufficient information regarding the microscopic state of the system. The structure of these measures are a key novelty that enables the derivation, and convergence to, autonomous McKean-Vlasov equations. The project will also establish new formulas for rare events through rigorous large deviations principles. These formulas will characterize how microscopic fluctuations can lead to large scale macroscopic changes. The results will be developed for practically important classes of random graphs (relevant for the visual cortex model and social models) that allow for state-dependent edge connections, and hypergraphs that arise in many-agent interaction models. 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.

NIH Research Projects · FY 2025 · 2025-09

Project Summary Fibroblasts play a crucial role in tissue function by generating and modulating contractile forces in response to environmental cues. Upon activation, they upregulate actin and myosin to enhance contractile force generation, a process vital for wound closure and tissue repair. However, prolonged fibroblast activation can lead to pathological outcomes, including fibrocontractile diseases. Traditional mechanobiology has identified four primary mechanical factors influencing fibroblast activation: ECM stiffness, ECM microstructure, ECM viscoelasticity, and mechanical stress magnitude. Our recent work introduces a fifth factor, stress anisotropy, which refers to directional stress differences (Nature Materials, 2024). Our findings show that stress anisotropy significantly impacts fibroblast activation and can be modulated. Furthermore, we demonstrate that stress anisotropy can induce cellular mechanical memory, allowing fibroblasts to retain an activated state long after the initial stress is removed (PNAS, 2024). Building on these discoveries, we will investigate the effects of stress anisotropy at the cellular, tissue, and clinical scales: Specific Aim 1 will explore strategies for controlling long-term fibroblast activation by reducing stress anisotropy. Using our advanced micropatterning technology, we will independently manipulate ECM stiffness, cell shape, cell area, and the magnitude and direction of stretching to test whether reducing stress field anisotropy can prevent prolonged fibroblast activation in clinical settings such as skin grafting. Specific Aim 2 will investigate the interplay of extracellular and intracellular factors in fibroblast responses to anisotropic stress fields. We hypothesize that changing ECM properties — due to aging or diseases like fibrosis — affects mechanical signal transmission, impacting fibroblast activation. Our in vitro model of cell-collagen tissues, which generates both isotropic and anisotropic stress fields within a single tissue specimen, will enable us to dissect these responses. Specific Aim 3 will focus on ex vivo studies to optimize skin grafting techniques by controlling stress anisotropy through meshing parameters. Traditional grafts are meshed with parallel slits and stretched to cover larger wounds. Our preliminary data suggest that modifying slit length and orientation can optimize tension anisotropy control. We will also investigate kirigami-inspired meshing, which may provide superior control over anisotropy, in skin grafting. Success in these aims will yield validated tools for stress measurement and control, refined surgical techniques, and clear markers of cellular response, advancing therapeutic strategies for skin grafting.

NSF Awards · FY 2025 · 2025-08

This collaborative project explores how a tiny droplet, powered by internal and surface activity, can propel itself—serving as a simplified model for how primitive cells, or "protocells", move. In experiments, such systems can be created by building networks of actin proteins inside and along the membrane of giant vesicles. To understand how this motion arises, the research team develops mathematical models that describe how forces inside the droplet and on its surface interact with the surrounding fluid. A key focus is to understand how this active droplet pushes against its environment to generate sustained forward motion—behavior that is fundamental to many forms of movement in soft materials and living cells. The project supports graduate education at Florida State University and New Jersey Institute of Technology, and promotes collaboration and dissemination of scientific knowledge through scientific workshops and seminars. The project aims to elucidate the role of steric alignment interactions in the nematic fluid on drop propulsion. The project combines analytical theory, numerical simulations, and comparisons with experimental data from active vesicle systems. The primary investigator Young leads the development of mathematical models and analytical methods, including theory of partial differential equations (PDE), dynamical systems analysis, differential geometry, and asymptotic techniques. The primary investigator Quaife develops efficient numerical algorithms for solving coupled surface-bulk PDEs on both rigid and deforming geometries. These numerical methods include solvers for surface PDEs on evolving interfaces and bulk-surface coupling across moving boundaries. A central challenge is modeling steric alignment interactions at the continuum level and calibrating their strength using experimental observations. The resulting framework has broad applicability to active matter systems described by coupled surface-bulk dynamics on moving domains. 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.