INTENSE REU Research Projects

Faculty Mentor:  Dr. Michaelann Tartis, Professor of Chemical Engineering 

This research study will utilize previously developed hydrogel formulations that match the mechanical properties (stiffness) of human cranial tissues to study the role of cavitation and shear deformation in simulated traumatic brain injuries (TBI). Hydrogel formulations will be optimized to include tissue mimics for both anisotropic white matter properties and isotropic grey matter properties, for both optical and acoustic imaging during an impact of a cranial phantom in a drop tower apparatus.  Optimization for simultaneous imaging requires these materials to be transparent, contain tissue-like sound scatter, and tissue-matched attenuation. The REU student will acquire images of high-rate loading experiments using high-speed digital cameras and high-frame rate ultrasound.  To understand which impact circumstances may result in cavitation induced injuries in mild to severe traumatic brain injuries, several parameters will be varied: impact energy, head orientation, location of ultrasound transducer, soft tissue models, and 3D printed skull geometries. The student will use MATLAB and ImageJ softwares to extract cavitation event number, location, persistence, and frequency over a given time period. The student will gain experience in acquiring and quantifying image datasets using image processing.  Research outcomes will include new understanding of impact-induced traumatic brain injury conditions and insights toward mitigation methods.

Faculty Mentor:  Dr. Ali Barenji, Assistant Professor of Mechanical Engineering

This project explores next-generation human–autonomy teaming (HAT) for collaborative robot manipulation using Extended Reality (XR) technologies. The system integrates immersive XR interfaces with autonomous robotic control to enable intuitive, real-time human guidance, task allocation, and situational awareness. Through mixed-reality visualization and haptic interaction, operators can train, supervise, and co-manipulate robotic systems in complex environments. The project aims to enhance human–robot coordination, adaptability, and decision-making while establishing a framework for safe, efficient, and human-centered autonomy in industrial and research applications.

Faculty Mentor:  Dr. Frank Austin Mier, Visiting Assistant Professor of Mechanical Engineering

This research project focuses on advancing the technique of Background Oriented Schlieren (BOS) imaging by developing and analyzing the effectiveness of structured background patterns. Traditional BOS imaging often employs randomized speckle patterns, but this project explores whether specific color and image patterns can streamline image processing while maintaining or enhancing quantitative accuracy. The REU student will design, fabricate, and test structured backgrounds using various experimental setups to investigate their impact on flow visualization and refractive index gradients. Key tasks include evaluating the clarity, resolution, and quantitative precision of BOS measurements with varying structured patterns under controlled experimental conditions. This work will expose students to optical imaging techniques, image processing software (e.g., MATLAB or Python), and experimental fluid dynamics principles. Research outcomes may contribute to improved BOS methodologies, enabling simpler and more efficient image analysis for applications in explosives engineering, fluid mechanics, and thermal sciences.

Faculty Mentor:  Dr. Wes Cook, Associate Professor of Civil Engineering

NMT has a sponsored project with Los Alamos National Laboratory to conduct fire experiments inside a scientific glovebox. The project includes setup of fires, remote ignition, HVAC installation and setup, instrumentation, safety, machining of parts, coding, material characterization, and analysis. The project provides exposure to multiple disciplines within Mechanical Engineering as well as Chemical, Electrical, and Civil Engineering. Selected student(s) have a chance to use existing skill sets, develop in new areas, and apply classroom knowledge in a research setting. Ideal candidates will also have an interest in working at a National Laboratory and begin making contacts. US citizenship is required.

Faculty Mentor:  Dr. Hassan Khaniani, Assistant Professor of Mineral Engineering

This project focuses on building a small-scale system that can image hidden cracks and voids underground using special fluid tracers and sensors. The idea is to inject a tracer (such as a magnetic, acoustic, or thermal fluid) into fractures and then detect how it moves using sensors placed inside a model well. The data collected are used to create tomographic images, similar to a medical CT scan, showing where fractures or empty spaces exist in the rock. This work combines hands-on sensor design, data collection, and imaging to explore how next-generation smart tracer systems could help the oil, gas, and mining industries better understand how fluids move underground—leading to safer, more efficient, and more sustainable resource development.