Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
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- Creators: Mu, Bin
Description
An accurate knowledge of the complex microstructure of a heterogeneous material is crucial for quantitative structure-property relations establishment and its performance prediction and optimization. X-ray tomography has provided a non-destructive means for microstructure characterization in both 3D and 4D (i.e., structural evolution over time). Traditional reconstruction algorithms like filtered-back-projection (FBP) method or algebraic reconstruction techniques (ART) require huge number of tomographic projections and segmentation process before conducting microstructural quantification. This can be quite time consuming and computationally intensive.
In this thesis, a novel procedure is first presented that allows one to directly extract key structural information in forms of spatial correlation functions from limited x-ray tomography data. The key component of the procedure is the computation of a “probability map”, which provides the probability of an arbitrary point in the material system belonging to specific phase. The correlation functions of interest are then readily computed from the probability map. Using effective medium theory, accurate predictions of physical properties (e.g., elastic moduli) can be obtained.
Secondly, a stochastic optimization procedure that enables one to accurately reconstruct material microstructure from a small number of x-ray tomographic projections (e.g., 20 - 40) is presented. Moreover, a stochastic procedure for multi-modal data fusion is proposed, where both X-ray projections and correlation functions computed from limited 2D optical images are fused to accurately reconstruct complex heterogeneous materials in 3D. This multi-modal reconstruction algorithm is proved to be able to integrate the complementary data to perform an excellent optimization procedure, which indicates its high efficiency in using limited structural information.
Finally, the accuracy of the stochastic reconstruction procedure using limited X-ray projection data is ascertained by analyzing the microstructural degeneracy and the roughness of energy landscape associated with different number of projections. Ground-state degeneracy of a microstructure is found to decrease with increasing number of projections, which indicates a higher probability that the reconstructed configurations match the actual microstructure. The roughness of energy landscape can also provide information about the complexity and convergence behavior of the reconstruction for given microstructures and projection number.
In this thesis, a novel procedure is first presented that allows one to directly extract key structural information in forms of spatial correlation functions from limited x-ray tomography data. The key component of the procedure is the computation of a “probability map”, which provides the probability of an arbitrary point in the material system belonging to specific phase. The correlation functions of interest are then readily computed from the probability map. Using effective medium theory, accurate predictions of physical properties (e.g., elastic moduli) can be obtained.
Secondly, a stochastic optimization procedure that enables one to accurately reconstruct material microstructure from a small number of x-ray tomographic projections (e.g., 20 - 40) is presented. Moreover, a stochastic procedure for multi-modal data fusion is proposed, where both X-ray projections and correlation functions computed from limited 2D optical images are fused to accurately reconstruct complex heterogeneous materials in 3D. This multi-modal reconstruction algorithm is proved to be able to integrate the complementary data to perform an excellent optimization procedure, which indicates its high efficiency in using limited structural information.
Finally, the accuracy of the stochastic reconstruction procedure using limited X-ray projection data is ascertained by analyzing the microstructural degeneracy and the roughness of energy landscape associated with different number of projections. Ground-state degeneracy of a microstructure is found to decrease with increasing number of projections, which indicates a higher probability that the reconstructed configurations match the actual microstructure. The roughness of energy landscape can also provide information about the complexity and convergence behavior of the reconstruction for given microstructures and projection number.
ContributorsLi, Hechao (Author) / Jiao, Yang (Thesis advisor) / Chawla, Nikhilesh (Committee member) / Liu, Yongming (Committee member) / Ren, Yi (Committee member) / Mu, Bin (Committee member) / Arizona State University (Publisher)
Created2017
Description
The accurate and fast determination of organic air pollutants for many applications and studies is critical. Exposure to volatile organic compounds (VOCs) has become an important public health concern, which may induce a lot of health effects such as respiratory irritation, headaches and dizziness. In order to monitor the personal VOCs exposure level at point-of-care, a wearable real time monitor for VOCs detection is necessary. For it to be useful in real world application, it requires low cost, small size and weight, low power consumption, high sensitivity and selectivity.
To meet these requirements, a novel mobile device for personal VOCs exposure monitor has been developed. The key sensing element is a disposable molecularly imprinted polymer based quartz tuning fork resonator. The sensor and fabrication protocol are low cost, reproducible and stable. Characterization on the sensing material and device has been done. Comparisons with gold standards in the field such as GC-MS have been conducted. And the device’s functionality and capability have been validated in field tests, proving that it’s a great tool for VOCs monitoring under different scenarios.
To meet these requirements, a novel mobile device for personal VOCs exposure monitor has been developed. The key sensing element is a disposable molecularly imprinted polymer based quartz tuning fork resonator. The sensor and fabrication protocol are low cost, reproducible and stable. Characterization on the sensing material and device has been done. Comparisons with gold standards in the field such as GC-MS have been conducted. And the device’s functionality and capability have been validated in field tests, proving that it’s a great tool for VOCs monitoring under different scenarios.
ContributorsDeng, Yue, Ph.D (Author) / Forzani, Erica S (Thesis advisor) / Lind, Mary L (Committee member) / Mu, Bin (Committee member) / LaBelle, Jeffery (Committee member) / Emady, Heather (Committee member) / Arizona State University (Publisher)
Created2017