CERT Seed Grant Program

To accelerate NJIT’s trajectory towards increased collaborative research and innovation funding for higher faculty and student success, a new strategic initiative, Collaborative Early Research Translation (CERT) Seed Grants supported by the U.S. National Science Foundation Accelerating Research Translation (ART) program was launched in 2023 to invest in translational research of high potential impact. These CERT seed grants will initiate early translation of research and innovation, working collaboratively with an external partner, towards developing proof-of-feasibility and potential intellectual property to build foundation to submit competitive proposals for external research translation acceleration funding opportunities, or internal NJIT TITA (Technology Innovation Translation Acceleration) seed grants for further advancement in translational research and market validation.

NJIT’s internal seed funding opportunities are critical components of the strategic Research, Innovation and Technology Entrepreneurship (RITE) ecosystem as outlined in the 2030 Strategic Plan. 

Announcement

Collaborative Early Research Translation (CERT) Seed Grant Awards FY2026 (July 2025 - June 2026)

Congratulations to All Awardees!

We are pleased to announce the award of 5 CERT Seed Grants for very exciting translational research projects with external collaborators and partners. Each CERT Seed Grant is awarded $25,000 with a total investment of $125,000 which is partially funded by the NSF Accelerating Research Translation (ART) award and through the NJIT Center for Translational Research (CTR). Congratulations to all recipients of the FY2026 CERT Seed Grants (listed below)!

Newark College of Engineering

NJIT Principal Investigator: Farid Alisafaei, Mechanical Engineering
Collaborator/Partner: Guy Genin, Professor
Collaborator/Partner Affiliation: Washington University St. Louis
CERT Project Title: DermaMech: Kirigami-Inspired Skin Grafts that Enhance Graft Success and Reduce Donor Site Morbidity
CERT Funding: $25,000
Executive Summary: Split-thickness skin grafting is vital for treating severe wounds affecting 8.2 million Americans annually, yet failure rates remain unacceptably high (7-66%) and are currently unpredictable. Our laboratory has identified the biomechanical mechanism underlying these failures: uneven distribution of mechanical stress within meshed grafts activates fibroblast cells, inducing a persistent "mechanical memory" effect that drives excessive contraction and graft failure. We have invented a technology for reengineering the meshing pattern to control this stress distribution and prevent adverse outcomes.

NJIT Principal Investigator: Rayan Hassane Assaad, Civil and Environmental Engineering
Collaborator/Partner: Wei Wang, CEO and founder Operating Officer
Collaborator/Partner Affiliation: UrbanTech Consulting Engineering
CERT Project Title: An Intelligent Unmanned Aerial Vehicle (UAV) Drone Robotic System Featuring Dynamic Sensor Reorientation Apparatus, Multimodal Sensor Fusion, and Artificial Intelligence (AI) for Smart and Automated Infrastructure Asset Management
CERT Funding: $25,000
Executive Summary: Unmanned Aerial Vehicles (UAVs) or drones are pivotal in the field of infrastructure inspection and monitoring, offering multiple operational benefits such as improved accessibility, enhanced efficiency, and increased safety. Despite the growing adoption of UAVs in infrastructure asset management applications, current systems often face challenges related to limited sensor adaptability and data collection/coverage capabilities, payload limitations, and inefficient power consumption ultimately reducing inspection fidelity and mission endurance. Therefore, there is a pressing need to enhance the adaptability, intelligence, and sensing capabilities of UAV-based robotic inspection platforms. This project proposes a novel UAV robotics system, designed with a rotating sensor belt mechanism – a Dynamic Sensor Reorientation Apparatus (DSRA) – to advance the monitoring setup by allowing sensors to move 360-degrees across the body of the drone, thus increasing its fidelity. This mechanism addresses key limitations associated with the multi sensor payload distribution by reducing the weight, optimizing energy usage and allowing full frame coverage of the targets without the need of extensive UAV maneuvering in complex tasks (i.e., inspection). In addition to the reorientation system, the proposed method integrates several emerging states of the art technologies such as multi model sensor fusion with AI algorithms (i.e., attention-based region proposal deep learning segmentation architecture) for accurate defect identification and proper quantification. This novel robotic system introduces new frontiers in both aerial hardware and software integration by combining RGB and thermal cameras with rangefinders for accurate quantification and an AI-driven two-stage deep learning architecture that first identifies critical inspection regions and then detects and quantifies defects with high accuracy. The research team will leverage the 3D printing and fabrication facilities in the NJIT Makerspace to develop the proposed platform. This innovation intersects with two clusters within the NJIT research enterprise: robotics and machine intelligence, and data science and management.

NJIT Principal Investigator: Xiaobo Li, Biomedical Engineering
Collaborator/Partner: Qinyin Qiu, Assistant Professor
Collaborator/Partner Affiliation: School of Health Professions, Rutgers University
CERT Project Title: Toward the Development of an Interactive Virtual Environment for Motor Activity Deficits Assessment and Rehabilitation in Children with ADHD
CERT Funding: $25,000
Executive Summary: Motor activity deficit is one of the key deficits in ADHD that manifests in tasks requiring sequential movements and fine-motor skills. The routine stimulant approach for ADHD symptom management has been widely concerned about its severe side-effects, high cost, and inefficiency in addressing the motor activity deficit in ADHD. Exergame-based intervention for motor activity deficit in ADHD, which is personalized, affordable, easily accessible, and with minimal side-effect, has not yet been sufficiently investigated. To address this press need in the field, the overall goal of this research program is to develop and disseminate a virtual reality (VR)- based exergame intervention platform, the Sequential Movement Assessment and Rehabilitation Toolkit (SMART), targeting the behavioral impairments and neurobiological substrates associated with motor activity deficit in ADHD. The short-term objectives of this pilot research focus on the development of SMART and testing its feasibility and reliability in healthy children. Based on the critical pilot results generated from this pilot research, we will apply for large-scale grants from federal agencies to conduct longitudinal and trail-based studies in sizable samples, to solidly validate the behavioral and neurobiological improvements associated with this innovative intervention approach in ADHD. The market need of our proposed product is pressing and sizable. The ADHD apps market is projected to grow significantly, with a global value of $1.7 billion in 2024, expected to reach $4.9 billion by 2033. This internal funding is needed for our collaborative research program from NJIT and Rutgers NJMS to address the high-impact scientific and societal needs of ADHD, which is one of the major general-public health concerns.

Jordan Hu College of Science and Liberal Arts

NJIT Principal Investigator: Sara Casado Zapico, Chemistry and Environmental Science
Collaborator/Partner: Victoria Dominguez, Assistant Professor
Collaborator/Partner Affiliation: Lehman College-CUNY
CERT Project Title: Bonepihist: new biomarkers for osteoporosis prediction and prognosis
CERT Funding: $25,000
Executive Summary: The goal of this proposal is to address one of the fundamental problems in the elderly: the correct diagnosis and prognosis of osteoporosis to improve quality of life in a vulnerable population. This will be approached by the identification of novel biomarkers through the simultaneous assessment of epigenetic and histological changes in bone tissues during the adult lifespan. The significance and market need of this proposal relies on the current medical costs associated with osteoporosis treatment and moreover the post-fracture hospitalization and recovery, which not only is an economic burden, also increases disability and reduces lifespan, significantly hampering healthy aging. The identification of novel biomarkers could lead to the development of preventive strategies and/or more targeted and less expensive treatments. The expected outcome of this proposal is that a two-way assessment of methylome-bone architecture, in combination with machine learning approaches, will identify specific bone aging biomarkers to address and potentially overcome the effects of OP. These outcomes are based on trial data generated by the PIs and the proven effectiveness of both techniques individually to evaluate bone aging. Our future plans will include the development of the first epigenetic clock for bone tissue, filing a patent with the newly discovered biomarkers, and applying for NIH, NSF and NIJ grants to target both clinical and anthropological questions of interest, based on the broader impacts of the findings of this project. Our justification for internal funding is to develop enough robust preliminary data to be able to apply for external federal funding.

Martin Tuchman School of Management

NJIT Principal Investigator: Jae-Hyuck Park, MTSM
NJIT Co-Principal Investigators: Jim Shi, MTSM
Collaborator/Partner: Bryan Santos, Director of Corporate Development at Aerofarms
Collaborator/Partner Affiliation: AeroFarms
CERT Project Title: AgriTech in the era of AI: Vertical Farming Innovation and Implications
CERT Funding: $25,000
Executive Summary: In the era of AI, Agricultural Technology (AgriTech) is rapidly advancing. Vertical farming (VF), an innovative form of AgriTech, has been widely implemented to meet the growing population’s needs by cultivating crops in vertically stacked layers within controlled environments using advanced agricultural techniques, such as IoT, Machine Learning, AI-driven tools. One close business example is AeroFarms, located in Newark, NJ. This project aims to investigate the operational innovativations, market dynamics and economic benefits of VF. The merit of this project lies in its potential to address the urgent need for sustainable transformation in AgriTech via leveraging AI. VF o!ers substantial advantages, including increased efficiency, yield stability, and reduced energy consumption. VF also strengthens coordination between farmers and retailers, promoting more resilient and sustainable agricultural supply chains. We will begin by quantifying VF’s gains in economic efficiency and reliability. Building on this analysis, we will develop a game-theoretical model to explore the dynamics of collaboration-competition among firms. The project will also deliver actionable insights and best practices for agribusinesses to adopt VF more electively. Partnering with AreoFarms, we aim to translate findings into AI-oriented solutions and data-driven business models tailored to the VF industry. Support from this internal funding is essential to translate this study into practical VF. This in turn will provide a basis for future external funding opportunities. This work can contribute significantly to the field of entrepreneurship, sustainable operations and supply chain management and pave the way for the broader adoption of advanced AgriTech.

This work was partially supported by a U.S. National Science Foundation Accelerating Research Translation cooperative agreement (TIP-2331429) and the NJIT Center for Translational Research. The opinions, findings, and conclusions, or recommendations expressed are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.