Congratulations to the funded 2025 Texas A&M Engineering Experiment Station (TEES) research collaborations.
$10,000 Award
TL-CHIPS
Project Title: Mitigating Thermal Hotspots in 3D Chip Stacks Using Vertically Aligned Carbon Nanotube Arrays
Team Members: Principal Investigator Chun-Wei Yao, Lamar University; Stephen L. Jones, Tarrant County College; Jenny Zhou, Lamar University; and Xuejun Fan, Lamar University
3D chip stacking has emerged as a promising solution for enhancing performance and reducing footprint. However, one of the most critical challenges in 3D integration is the formation of thermal hotspots due to increased power density and limited heat dissipation paths. This project proposes the use of vertically carbon nanotube arrays to enhance vertical heat conduction in 3D chip stacks. The project will proceed in two phases: Simulation Phase (Lamar University): Thermal modeling of 3D chip architectures incorporating CNTs with different conjunction materials will be conducted to identify optimal configurations for minimizing hotspot formation. Prototype Phase (Tarrant County College): A physical prototype will be developed by growing CNTs on a dummy chip, a fabricated chip that mimics the physical and thermal characteristics of a real chip. Various seed/conjunction materials between the silicon wafer and CNTs (e.g., copper, Aluminum) will be studied to evaluate their impact on heat transfer and hotspot mitigation. During the prototype phase at Tarrant County College, an infrared camera will be used to monitor and analyze thermal behavior on the chip. The thermal resistance of the contact interface between the silicon substrate and the metal catalyst film for carbon nanotubes will be studied during the simulation phase. It will provide practical training opportunities to students from Tarrant County College and the Tarrant County College-Texas A&M Engineering Academy, and Lamar University, helping to strengthen Texas semiconductor workforce.
$7,500 Award
E-SIMS (Elder Symptoms Individualized Monitoring Systems)
Project Title: Real-Time Multimodal Predictive Analytics Framework for Healthcare Monitoring
Team members: Principal Investigator Haitham Adarbah, Texas A&M University-Kingsville; James Henry, Lamar University; Ngozi Mbue, Texas Woman’s University; Anika Rimu, East Texas A&M University; and Jabia Chowdhury, Texas a&M University-Texarkana
In 2024 approximately 4.1 million Americans turned 65 joining 18% of the current population that is 65+. Our current healthcare system, especially for elderly is largely reactive, with treatment often beginning only after symptoms appear and are reported by the patient. This can lead to delays in treatment and poorer outcomes, as patients are often unreliable narrators. While technologies like smartwatches and medical sensors are widely used, they typically function in isolation and provide fragmented information. For example, a heart monitor may detect changes in heart rate, but it cannot relate that to mobility or behavioral patterns. Furthermore, most AI-based health tools tend to focus on one-size-fits-all models that fail to reflect individual patient differences, treating patients with complex health conditions the same as healthy individuals. This lack of personalization, combined with limited healthcare resources, makes early detection and timely intervention difficult. To address these limitations, we propose a Real-Time Multimodal Predictive Analytics Framework for elderly healthcare monitoring. This system integrates diverse data types- including physiological signals, electronic medical records, imaging, patterns, and even audio/video into a unified platform. It features adaptive, patient-specific modeling that learns and evolves with individual’s health profile. Leveraging edge computing, the framework processes data directly on wearable or mobile devices, providing real-time insights to patients and providers.
Automated Pavement Construction
Project Title: Automating Geomaterials for Smarter Pavement Construction
Team members: Principal Investigator Vinay Krishnan, Texas A&M Engineering Experiment Station; Ayman Elzohairy, East Texas A&M University; Olugbenro Ogunrinde, Tarleton State University; and Jianxin Huang, Texas A&M University
The automation of geomaterial testing and construction processes has transformed pavement construction by integrating advanced technologies such as robotics, sensors, and real-time data acquisition systems. Automated laboratory testing using robotic systems and embedded sensors ensures consistent and accurate assessment of material properties. During construction, intelligent sensors can continuously monitor parameters such as plasticity index, density, stiffness, and strength, enabling real-time decision-making and minimizing the risks of human error. These technologies significantly enhance the quality and reliability of the data collected throughout the construction process.Field implementation of automation extends to quality assurance and control through the use of nondestructive testing (NDT) methods, including ultrasonic testing, Spectral Analysis of Surface Waves (SASW), lightweight deflectometers, and needle penetrometers. Moreover, drones and robotics are increasingly employed for site surveying, material placement, and construction monitoring. For instance, drones equipped with thermal imaging can detect surface irregularities by identifying temperature anomalies. Additionally, the use of 3D printing technologies to place geomaterials not only ensures precision in material application but also improves construction efficiency, reduces rework, and delivers significant cost savings by streamlining operations and enhancing overall project quality.
$5,000 Award
NEXUS-X
Project Title: Integrated Innovative Nuclear Microreactor Systems for Resilient Water-Energy-Food Infrastructure in Texas
Team members: Principal Investigator Alexandru Herescu, Tarleton State University; Qiang Xu, Lamar University; Jafaru Egieya, Texas A&M Energy Institute; and Yassin Hassan, Texas A&M Engineering Experiment Station
Texas is facing a projected 150% surge in energy demand from data centers alone over the next decade. This is compounded by population increase, mounting climate risks, increased oil and gas activities, and rising pressure on water and food systems, especially during disasters like droughts, power outages, and extreme weather. Our project, NEXUS-X, proposes the development, simulation, and optimization of an integrated energy system powered by mobile nuclear heat pipe microreactors. These microreactors are compact, transportable, and offer high-density, low-carbon energy suitable for steady-state and transient operations. NEXUS-X envisions deploying these systems to support a wide range of applications including: (1) Reliable power for data centers; (2) Desalination and hydrogen production; (3) Remote and rural water-energy-food (WEF) infrastructure; (4) Industrial use (e.g., chemical production); (5) Space exploration. We will combine: (i) System dynamics modeling to simulate interconnected WEF sectors; (ii) Thermal-fluid optimization of the heat pipe microreactor for safety and efficiency; (iii) Multi-objective optimization across economic, environmental, and social goals.
REACT-2-FIRE
Project Title:Real-time AI-Connected Digital Twin for the Prediction of Environmental and Physiological Risks in Firefighters in High-Hazard Zones
Team members: Principal Investigator Adedeji Afolabi, Tarleton State University; Avdesh Mishra, Texas A&M University-Kingsville; Ellie Rahbar, Texas A&M University; and Noemi Mendoza, Texas A&M University
Firefighters operate in extreme environments without access to real-time monitoring tools that can detect physiological stress or environmental hazards early. This results in an annual cost of firefighter injuries estimated to be around $6 billion. The lack of real-time data-driven insight leads to preventable injuries, delayed emergency responses, and increased operational costs. The goal of this project is to develop a AI-enabled React-2-Fire tool that introduces a cutting-edge digital twin platform that integrates real-time physiological (e.g., heart rate, blood oxygen, motion) and environmental (e.g., air quality, temperature, location) sensor data. This system will combine multiple predictive analytics of biomarkers and environmental conditions to identify a holistic risk threshold based on the individuals’ digital twins to limit stress or injury risks as they arise, providing actionable insights to enhance firefighter safety.
SmartHeal
Project Title: Development of Smart Self-Healing Materials Using AI-Guided Multi-Material/Phase Additive Manufacturing
Team members: Principal Investigator Abolghassem Zabihollah, Tarleton State University; Nourouddin Sharifi, Tarleton State University; Ebrahim Seidi, Lamar University; Suleiman Obeidat, Sam Houston State University; and Robert Kelley Bradley, Lamar University
Bone fractures, cuts, and similar injuries are common health issues, especially under harsh working conditions. The human body has evolved to naturally respond to such situations through self-diagnosis and self-healing mechanisms to prevent more severe complications, such as healing broken bones, forming blood clots, and repairing skin tissue. We plan to mimic the nature by developing intelligent, self-healing materials designed for biomedical applications by combining AI with multi-material/phase 3D printing. The developed materials can sense damage and respond adaptively, mimicking how the human body heals itself. By integrating AI, we aim to create smarter, more responsive systems and optimize the process and the products. In addition to biomedical applications, this new generation of materials has the potential applications in aerospace and automotive industries.
Solar Fab Watchdog
Project Title:Next generation PV manufacturing process for cost reduction and efficiency improvement
Team members: Principal Investigator Anthony Hill, Prairie View A&M University; Xianchang Li, Lamar University; Velumani Suramaniam, Texas A&M University; Emmanuel Dada, Prairie View A&M University; and Ibrahim Gunes, Texas A&M University
This project proposes to develop a cutting-edge Smart PV (Photovoltaic) manufacturing process to reduce production costs while improving efficiency. The method introduces a new fab process to modify conventional processing techniques. Integrating sophisticated automation with intelligent process control to resolve typical cost & efficiency problems in traditional PV production lines. For example in the next generation Si-CIGS and Si-Perovskite based tandem solar cells, we can implement the hybrid deposition process like Spray deposition-Co-Evaporation-spin coating. In details, we will make effort to introduce the following: 1. Identifying critical stages of the manufacturing process to reduce materials / labor costs and human error 2. Developing and implementing a prototype supplement manufacturing facility by adapting hybrid deposition to reduce the cost. 3. Integrating industry 4.0 technologies including machine learning, real time process monitoring and predictive maintenance.
$2,500 Award
Texans-in-the-Loop
Project Title:GridWISE: AI and Human-Centered Optimization for Resilient Renewable Energy Communities
Team members: Principal Investigator Md Monirul Islam, Texas A&M University-Kingsville; Abdallah Farraj, Texas A&M University-Texarkana; Nuri Yilmazer, Texas A&M University-Kingsville; and Baisravan HomChaudhuri, Lamar University
One of the primary challenges facing today’s electrical grid is ensuring system resiliency while enabling community energy independence from the grid and supporting long-term net-zero society. In particular, the electrical grid and community level microgrids must become more adaptive and robust in the face of natural disasters and climate-related disruptions. Additional challenges arise from the integration of conventional neighborhoods and smart homes with intelligent appliances. Addressing these challenges requires a transformative approach—designing the microgrid with distributed energy sources and developing intelligent operation plan by leveraging agentic artificial intelligence (AI) in coordination with human-in-the-loop strategies. By optimal microgrid design and embedding goal-driven, self-improving AI agents within a prosumer-based community energy market, the power system can achieve proactive, decentralized decision-making. Each agent will operate autonomously to optimize local energy use and generation, while a higher-level system will ensure coordinated, system-wide optimization focused on minimizing energy costs and dependence on electric grid, and maximizing community welfare. This approach directly addresses both environmental and operational complexity, enabling resilient, adaptive, and sustainable energy systems of the future.
UrbanShield
Project Title: SIEMS: Secure and Intelligent Emergency Management System
Team members: Principal Investigator Erdogan Dogdu, Angelo State University; Anitha Chennamaneni, Texas A&M University – Central Texas; Lavanya Elluri, Texas A&M University – Central Texas; Deepti Gupta, Texas A&M University – Central Texas; and Jamil Ahmed, Texas A&M University – Texarkana
Disaster or any emergency management cannot be done efficiently and effectively as we have seen many times, like in California fires, with delayed emergency alerts, lack of coordination among agencies and no intelligence built in the systems. Current systems are not connected and not interoperable. And, AI is not part of these systems. We will develop: – A unified data model, based on “knowledge graphs”, with data from sensors, logs, policy documents. – An intelligent system, with specific AI models addressing the needs of different users/agencies like “first responders”, “city/state/federal agencies”, “researchers/universities”, and more. AI models will integrate explainable AI techniques (XAI) so that decisions are explained. – A smart alert system communicating with users/agencies in real-time. – LLM-based chatbot for all users interacting with the data/system. – Integrated “security” and “privacy” methods to ensure that the system provides required cybersecurity protections and privacy measures, and provides actionable insight securely overall.
A2Map
Project Title: AI-AR Framework for In-Situ Melt Pool Prediction and Visualization
Team members: Principal Investigator Wenhua Yang, Prairie View A&M University; Lai Jiang, Prairie View A&M University; Hongbo Du, Tarleton State University; Hoe-Gil Lee, Tarleton State University; Md Nizam Uddin, Texas A&M University-Texarkana; and Pratyush Kumar, Texas A&M University
Additive manufacturing (AM) enables complex, rapid prototyping. Melt pool affects various AM component properties (e.g., porosity and microstructure). Current melt pool monitoring is reactive and lacks intuitive feedback. Therefore, a predictive and immersive tool to monitor and control melt pool behavior in real time is strongly required. The overarching goal of this project is to integrate AI (Artificial intelligience) and AR (Augmented Reality) to predict and visualize melt pool size dynamics during AM. The expected outcome will be real-time melt pool size maps and feedback to maintain process stability and quality. The key objectives are: (1) Train AI models to predict melt pool size using sensor data (thermal). (2) Develop AR overlays to display melt pool size and deviations live during printing. (3) Enable process parameter adjustment based on AI-AR feedback. (4) Validate system through comparative experiments. Deep learning model will be trained using data of past melt pool information to predict future melt pool information. Live spatial melt pool maps will be overlayed on the printed parts. Low-latency architecture will be adopted to close the loop between AI prediction and AR visualization.
Geo-guardians
Project Title: Geosynthetic Solutions for Transportation Infrastructure under Extreme Weather Events
Team members: Principal Investigator Shihao Huang, Tarleton State University; Balaji Lakkimsetti, Texas A&M University; and Puneet Bhaskar, Texas A&M University
In recent times, the severity and frequency of extreme weather events including flooding, hurricanes, droughts, and freeze-thaw cycles have increased significantly. Due to these natural hazards, the vulnerability of transportation infrastructure, particularly roads, highways, and bridges, has become a critical concern. Furthermore, these events result in billions of dollars in maintenance and rehabilitation costs, service disruptions, and even loss of life. As such, there is a pressing need for cost-effective and resilient solutions that can protect critical infrastructure. Geosynthetics have been proven to offer effective solutions. However, their optimal application is often complicated by several factors namely, variability in soil conditions, regional climate differences, and infrastructure types. To facilitate practical and effective solutions for transportation infrastructure susceptible to hazards, this project proposes to develop a simplistic framework that maps geosynthetic solutions with civil infrastructure types, soil types, and hazard type. The research team will collect and analyze comprehensive data on soil types, geographic and climatic conditions, and existing infrastructure vulnerabilities. The pilot study will be conducted for the Texas region and can be extended to other parts of the country. Texas serves as a prime example of this complexity with wide range of soil types – from expansive clays in the east, to sandy loams in the west. A life cycle cost analysis will be conducted to compare various geotechnical reinforcement techniques with the use of various geosynthetic products.
