The present work describes a new morphology of cylindrical graphite synthesized in a unique manner by direct solar decomposition of methane and reports the mechanical properties of its manifestation in woven carbon–carbon composites. Whereas traditional carbon–carbon composites are formed via epoxy impregnation, the deposited material in the process presented here conformally follows the existing shape and orientation of individual carbon fibers. Through this process, cylindrical graphite fibers are synthesized that possess superior strength due to their graphitic layers that amalgamate into interlocked pathways between disparate fibers. Strength measurements, taken in tandem with Raman, XRD, and SEM, paint a picture of the shift from cloth to composite behavior via graphitization of the original substrate combined with sheets of graphene coalescing into a unified composite, with a notable improvement in elastic modulus from 0.19 to 2.66 GPa.

Accurate depth perception is essential in teleoperated surgery, where surgeons depend on clear visual and haptic feedback to perform delicate, high-precision movements. In this work, we present the Teleoperated Enhanced Robotic Surgical Assistant (TERESA), a system designed to improve how operators perceive and interact with remote surgical environments compared with legacy platforms such as the earliest Da Vinci systems. TERESA uses a pair of stereo cameras to provide distinct left- and right-eye views through a virtual reality headset, while synchronizing the user’s head movements directly to camera motion with very low latency. By coupling visual input with natural head movement, the system reduces sensory mismatch and helps limit discomfort such as eye strain, disorientation, and motion sickness. This alignment also makes control more intuitive, allowing operators to guide the robot simply by looking toward the area they wish to inspect or approach. To enable this capability, we developed a 5-axis robotic platform that supports precise positioning and orientation of the camera system. In this paper, we describe the overall mechanical design and development process, including hardware improvements that produce smoother, more stable, and more reliable motion throughout the robotic platform. Our goal is to show that synchronizing vision and movement can improve depth perception, operator comfort, and ease of control, underscoring TERESA’s potential to support safer, more effective, and more intuitive surgical robotics.

Stormwater often carries persistent pollutants such as PFAS, and biofilters are increasingly used to treat it, though the microbial processes driving their effectiveness are not fully understood. This project examines how bacterial communities and their functional roles change after stormwater passes through biofilters, with a focus on nitrogen cycling processes like nitrification, denitrification, and nitrogen fixation. Biofilter effluent samples are filtered through 0.22 μm membranes, and DNA is extracted using the FastDNA™ SPIN Kit for Soil. Quantitative PCR (qPCR) is used to measure the abundance of 16S rRNA and functional genes including nirK, narG, nifH, and amoA, with results analyzed in Excel to compare across conditions. Findings show that nifH and nirK have the highest abundance, indicating strong nitrogen fixation and denitrification, especially in the ESCS (non-submerged) biofilter, which supports more diverse nitrogen cycling. In contrast, the ESCS_S (submerged) system favors reduction processes, with higher narG and nxrB activity, suggesting more anaerobic conditions. Sand filters show lower and more variable gene abundance, indicating less efficient microbial activity. Future work will analyze samples under varying wet and dry conditions. Overall, this study highlights the role of microbial processes in improving biofilter performance and advancing sustainable water treatment.

To assist with dune restoration for Santa Monica’s beaches, this study investigates how well Abronia maritima, Abronia umbellata, Ambrosia chamissonis, and Camissoniopsis cheiranthifolia trap sand. It is hypothesized that Ambrosia chamissonis will trap the most sand due to its larger, bushier structure, while Camissoniopsis cheiranthifolia will show moderate performance, and both Abronia species will trap less sand due to their more compact forms. To test this, the project isolates the effects of plant structure through 3D-printed models in a wind tunnel before comparing results with live plants. Current work focuses on prototyping these CAD models, with multiple design iterations improving printability by reducing excessive supports and minimizing assembly. Because these native species have not been previously tested under controlled conditions, this research evaluates their effectiveness at sand trapping and supports more ecologically sustainable dune restoration to protect coastlines from flooding.

Recent studies in the field of stimuli-responsive materials have highlighted their potential use in soft robotics, which have broad and rapidly growing applications in areas such as healthcare, manufacturing, and environmental monitoring. Soft robots are of great interest due to their mechanical performance, programmable nature, and low economic cost as a result of cheap materials and fabrication methods. Our research is focused on developing a humidity-responsive spherical soft robot with autonomous behavior that can function with both a position and time controlled release. This will be done by studying our employed polyvinyl alcohol (PVA) and carboxymethyl cellulose (CMC) hydrogel as a thin film, and the effect of constituent ratios, substrate material, rotation speed, and surfactant content on its deformation behavior. Using spin coating and systematic parameter variation, we determined that a combination of 6% PVA with a ratio of three parts PVA to one part CMC, cured at 60°C, on a polystyrene substrate exhibits the greatest deformation, making it the ideal recipe for our soft robot with deformation-based locomotion. Additionally, we discovered that spin coating on a polystyrene substrate with 7.5–10% surfactant content at 750–1000 RPM yields the best deformation patterns. These findings establish a reproducible material–process relationship that can inform the design of future stimuli-responsive soft robotic systems using the same recipe.