Browsing by Author "Ren, Xiang"

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    Multiscale Modeling of CNT-Polymer Nanocomposites and Fuzzy Fiber Reinforced Polymer Composites for Strain and Damage Sensing
    Ren, Xiang (Virginia Tech, 2014-05-06)
    It has been observed that carbon nanotube (CNT)-polymer nanocomposite material has observable piezoresistive effect, that is to say that changes in applied strain may induce measurable changes in resistance. The first focus of the work is on modeling the piezoresistive response of the CNT-polymer nanocomposites by using computational micromechanics techniques based on finite element analysis. The in-plane, axial, the three dimensional piezoresistive responses of the CNT-polymer nanocomposites are studied by using 2D, axisymmetric, and 3D electromechanically coupled and multiscale finite element models. The microscale mechanisms that may have a substantial influence on the overall piezoresistivity of the nanocomposites, i.e. the electrical tunneling effect and the inherent piezoresistivity of the CNT, are included in microscale RVEs in order to understand their influence on macroscale piezoresistive response in terms of both the normalized change in effective resistivity and the corresponding effective gauge factor under applied strain. The computational results are used to better understand the driving mechanisms for the observed piezoresistive response of the material. The second focus of the work is on modeling the piezoresistive response of fuzzy fiber reinforced polymer composites by applying a 3D multiscale micromechanics model based on finite element analysis. Through explicitly accounting for the local piezoresistive response of the anisotropic interphase region, the piezoresistive responses of the overall fuzzy fiber reinforced polymer composites are obtained. The modeling results not only provide a possible explanation for the small gauge factors as observed in experiments, but also give guidance for the manufacture of fuzzy fiber reinforced polymer composites in order to achieve large, consistent, and predictable gauge factors. The third focus of the work is on modeling the coupled effect between continuum damage and piezoresistivity in the CNT-polymer nanocomposites by using computational micromechanics techniques based on a concurrent multiscale finite element analysis. The results show that there is a good correlation between continuum damage and piezoresistive response of the nanocomposites, which gives theoretical and modeling support for the use of CNT-polymer nanocomposites in structural health monitoring (SHM) applications for damage detections.
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    Nonparametric Bayesian Functional Clustering with Applications to Racial Disparities in Breast Cancer
    Gao, Wenyu; Kim, Inyoung; Nam, Wonil; Ren, Xiang; Zhou, Wei; Agah, Masoud (Wiley, 2024-01)
    As we have easier access to massive data sets, functional analyses have gained more interest. However, such data sets often contain large heterogeneities, noises, and dimensionalities. When generalizing the analyses from vectors to functions, classical methods might not work directly. This paper considers noisy information reduction in functional analyses from two perspectives: functional clustering to group similar observations and thus reduce the sample size and functional variable selection to reduce the dimensionality. The complicated data structures and relations can be easily modeled by a Bayesian hierarchical model due to its flexibility. Hence, this paper proposes a nonparametric Bayesian functional clustering and peak point selection method via weighted Dirichlet process mixture (WDPM) modeling that automatically clusters and provides accurate estimations, together with conditional Laplace prior, which is a conjugate variable selection prior. The proposed method is named WDPM-VS for short, and is able to simultaneously perform the following tasks: (1) Automatic cluster without specifying the number of clusters or cluster centers beforehand; (2) Cluster for heterogeneously behaved functions; (3) Select vibrational peak points; and (4) Reduce noisy information from the two perspectives: sample size and dimensionality. The method will greatly outperform its comparison methods in root mean squared errors. Based on this proposed method, we are able to identify biological factors that can explain the breast cancer racial disparities.
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    Scalable nanolaminated SERS multiwell cell culture assay
    Ren, Xiang; Nam, Wonil; Ghassemi, Parham; Strobl, Jeannine S.; Kim, Inyoung; Zhou, Wei; Agah, Masoud (Springer Nature, 2020)
    This paper presents a new cell culture platform enabling label-free surface-enhanced Raman spectroscopy (SERS) analysis of biological samples. The platform integrates a multilayered metal-insulator-metal nanolaminated SERS substrate and polydimethylsiloxane (PDMS) multiwells for the simultaneous analysis of cultured cells. Multiple cell lines, including breast normal and cancer cells and prostate cancer cells, were used to validate the applicability of this unique platform. The cell lines were cultured in different wells. The Raman spectra of over 100 cells from each cell line were collected and analyzed after 12 h of introducing the cells to the assay. The unique Raman spectra of each cell line yielded biomarkers for identifying cancerous and normal cells. A kernel-based machine learning algorithm was used to extract the high-dimensional variables from the Raman spectra. Specifically, the nonnegative garrote on a kernel machine classifier is a hybrid approach with a mixed nonparametric model that considers the nonlinear relationships between the higher-dimension variables. The breast cancer cell lines and normal breast epithelial cells were distinguished with an accuracy close to 90%. The prediction rate between breast cancer cells and prostate cancer cells reached 94%. Four blind test groups were used to evaluate the prediction power of the SERS spectra. The peak intensities at the selected Raman shifts of the testing groups were selected and compared with the training groups used in the machine learning algorithm. The blind testing groups were correctly predicted 100% of the time, demonstrating the applicability of the multiwell SERS array for analyzing cell populations for cancer research.