The pre-recorded talks and posters on this page showcase the work of students who received NC Space Grant research funding during 2021-2022. The menu at right provides links to pre-recorded talks and posters by other funded students on additional topics.

Genesis Higueros

2021-22 Faculty Research Grant Student Researcher
Duke University
Graduate Student (Doctoral), Mechanical Engineering and Materials Science

Vascular Enabled Advanced (VENA) Batteries for Fast-Charging Electric Vehicles

Conventional battery electrodes are limited by a highly tortuous framework reducing lithium-ion diffusion and diminishing capacity. Three-dimensional (3D) battery electrodes have recently accumulated interest due to high surface areas and increased electrode thickness permitting greater energy and power densities. However, an optimal porous configuration for fast-charging batteries has yet to be discovered and synthesized. Inspired by the natural vascular transport system, Vascular ENabled Advanced (VENA) batteries offer increased charge transport through alteration of pore configuration alone. VENA batteries are realized through a forward prediction process that is machine learning-enabled and an inverse design of the optimal vascular configuration. The predicted design is then fabricated by photolithography micro-manufacturing of sacrificial vascular templates. Pre-magnetization of sacrificial templates allows preferred directional alignment in electrode slurry and vascular channel formation during sintering of electrodes. COMSOL battery simulations of several pore configurations including vertical channels and homogenous matrix (no channels) suggest VENA-batteries outperform all other battery cells in capacity, lithium-ion penetration depth, and tortuosity. Scanning electron microscope images confirm preferred directional alignment of vascular templates and vascular channels post-sintering in lithium cobalt oxide electrodes. Ultimately, the VENA design process may be applied to any porous energy storage and catalyst devices with limiting ionic kinetics.

Faculty Advisor: Po-Chun Hsu, Duke University

Elliot Paul

2021-22 NC Space Grant Undergraduate Research Scholar
East Carolina University
Undergraduate Student (Senior), Engineering

Implementation of an In Silico Modeling Pipeline for Tibial Remodeling in Microgravity

As space exploration becomes more prevalent, bone health in microgravity remains a major concern. Microgravity puts astronauts at risk of losing 1% – 1.5% of bone mass per month in space [1]. Researchers must better understand the pathways behind mechanically induced bone remodeling so that measures can be taken to protect astronauts’ bones. This study aimed to implement a mechanistic in silico approach to simulate mechanically-induced bone remodeling in microgravity. 

A finite element model was generated from an open-source geometry file of a healthy tibia. This model was meshed and analyzed with finite element analysis (FEA) in the open-source software FEBio [2] which was run using a MATLAB script and the open-source toolbox GIBBON. The output of this finite element analysis became the input to an existing NASA toolchain for bone remodeling in microgravity [3], which couples differential equations for the temporal evolution of biological and chemical factors. The model includes osteoblasts, osteoclasts, TGF-β1, PGE2, PTH, the RANK-RANKL-OPG pathway, and many more important factors. 

FEA results demonstrate steady bone decay as is experienced by astronauts in vivo. For example, when the mechanical loading was increased, the population of active osteoblasts and bone density slightly increased, while the number of active osteoclasts slightly decreased. The fully verified and validated model can aid in designing exercise protocols that minimize astronaut bone loss and maximize the potential for safe space exploration. 

Faculty Advisor: Ali Vahdati, East Carolina University

Roger Sachan

2021-22 Faculty Research Grant Student Researcher
North Carolina State University
Undergraduate Student (Senior), Biochemistry

3D Printing of Polytetrafluoroethylene for Medical Applications

Point of use fabrication of medical devices in space is an important focus of human spaceflight research. This project studied processing and characterization of polytetrafluoroethylene hollow needle arrays for the delivery of drugs or vaccines via the poke and flow mechanism. This delivery mechanism involves the movement of a drug or vaccine through the bore of a hollow needle that is placed on the surface of the skin. A digital micromirror device 3D printing process and subsequent sintering process was used to create needle arrays from a precursor containing polytetrafluoroethylene. Confocal laser scanning microscopy was used to assess the microstructure of the three-by-one hollow needle array; uniform heights, hollow bores, and sharp tips were noted in the 3D printed polytetrafluoroethylene needles. X-ray photoelectron spectroscopy was used to examine the elemental composition of the 3D printed polytetrafluoroethylene, and Raman spectroscopy was used to evaluate the carbon bonding of the 3D printed polytetrafluoroethylene; these studies indicated that the 3D printed polytetrafluoroethylene was similar to bulk polytetrafluoroethylene. Nanoindentation was used to assess the reduced elastic modulus of the 3D printed polytetrafluoroethylene. The reduced elastic modulus of the material was 1.94+/-0.22 GPa; this elastic modulus value is sufficient for penetration of human skin. The 3D printed polytetrafluoroethylene needle array was used to deliver methyl blue, which served as a model drug, to surgically-discarded human abdomen skin. Our results show that 3D printed polytetrafluoroethylene is appropriate for producing needle arrays and other point-of-use medical devices. 

Faculty Advisor: Roger Narayan, North Carolina State University

Jack van Welzen 

NIA/NASA Internship Award at Langley Research Center – Summer 2021
North Carolina State University
Graduate Student (Doctoral), Mechanical Engineering

Hidden Damage Visualization Using Laser Speckle Photometry

This lightning talk explores laser speckle photometry (LSP), a non-contact optical-based image analysis technique, for effectively and rapidly imaging hidden damage in structures. This technique demonstrates a promising potential for large-area inspection of composite structures in near real-time to unearth barely visible impact damage (BVID) which would typically go unnoticed during routine inspections. Proposed image algorithms (MSE, NCC, and SSIM) in conjunction with an optimal window size were tested on BVID in a honeycomb composite panel under thermal excitation. A low coherence (high-power) laser for fast screening of a large area was used to demonstrate the efficacy of LSP. The damage image region agreed with those from the point-by-point CT-scan with all three image algorithms. Among these three algorithms, SSIM generally is the method of choice. LSP with the proposed imaging conditions shows enormous potential as a real-time non-destructive inspection (NDI) tool not only in the aerospace industry but also in industries such as additive manufacturing where on-line in-situ monitoring is desired for prevalent defects. The real-time inspection using LSP will further allow immediate feedback for process controls.

Faculty Advisor: Fuh-Gwo Yuan, North Carolina State University

Nia Wilson

2021-22 NC Space Grant Undergraduate Research Scholar
East Carolina University
Undergraduate Student (Senior), Mechanical Engineering

An Improved Method for Comparing Existing and Observed Temperature Profiles for Use in Atmospheric Acoustic Propagation Model

This work contributes to an ongoing effort seeking to understand the effects of various factors on outdoor sound propagation. Specifically, this project examines the relationship between temperature and elevation and how it changes with time. Prior work measured temperature using sensors mounted on an unmanned aerial vehicle (UAV). Results from a model that simulates acoustic transmission loss were compared using the measured temperature gradients and using a simple linear temperature gradient assumption. That work concluded that temperature assumptions are insufficient at capturing the near-surface complexities, below about 5 m AGL (above ground level). These findings also brought up two main questions addressed in this work. First, what is a better way to capture the very near surface complexities than using a UAV? The UAV does an adequate job at capturing temperature data above 5 m but does not do a great job of detecting its vertical position near the ground because of its own positioning system uncertainty. To avoid this, a static array of thermocouples was implemented to measure the temperature gradient up to 5 m AGL. Then, the UAV and static array measurements were combined for a single temperature gradient up to 100 m for use in acoustic transmission loss simulations. These measurements were taken over different ground surfaces and a variety of weather conditions. A flight cycle is a pre-programmed vertical path that the UAV conducts. This path begins at 5 m in elevation, goes to 100 m, and then back to 5 m, which takes about 200 seconds. The second question is how does the temperature gradient change from cycle to cycle? Code was developed to determine the change in vertical temperature gradient with time. Understanding this question will help us answer the broader question of how accurate near-surface temperature information improves acoustic propagation modeling efforts.

Faculty Advisor: Teresa Ryan, East Carolina University