Browsing by Author "Nam, Wonil"

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    Bio-interfaced Nanolaminate Surface-enhanced Raman Spectroscopy Substrates
    Nam, Wonil (Virginia Tech, 2022-03-30)
    Surface-enhanced Raman spectroscopy (SERS) is a powerful analytical technique that combines molecular specificity of vibrational fingerprints offered by Raman spectroscopy with single-molecule detection sensitivity from plasmonic hotspots of noble metal nanostructures. Label-free SERS has attracted tremendous interest in bioanalysis over the last two decades due to minimal sample preparation, non-invasive measurement without water background interference, and multiplexing capability from rich chemical information of narrow Raman bands. Nevertheless, significant challenges should be addressed to become a widely accepted technique in bio-related communities. In this dissertation, limitations from different aspects (performance, reliability, and analysis) are articulated with state-of-the-art, followed by how introduced works resolve them. For high SERS performance, SERS substrates consisting of vertically-stacked multiple metal-insulator-metal layers, named nanolaminate, were designed to simultaneously achieve high sensitivity and excellent uniformity, two previously deemed mutually exclusive properties. Two unique factors of nanolaminate SERS substrates were exploited for the improved reliability of label-free in situ classification using living cancer cells, including background refractive index (RI) insensitivity from 1.30 to 1.60, covering extracellular components, and 3D protruding nanostructures that can generate a tight nano-bio interface (e.g., hotspot-cell coupling). Discrete nanolamination by new nanofabrication additionally provides optical transparency, offering backside-excitation, thereby label-free glucose sensing on a skin-phantom model. Towards reliable quantitative SERS analysis, an electronic Raman scattering (ERS) calibration method was developed. ERS from metal is omnipresent in plasmonic constructs and experiences identical hotspot enhancements. Rigorous experimental results support that ERS can serve as internal standards for spatial and temporal calibration of SERS signals with significant potential for complex samples by overcoming intrinsic limitations of state-of-art Raman tags. ERS calibration was successfully applied to label-free living cell SERS datasets for classifying cancer subtypes and cellular drug responses. Furthermore, dual-recognition label-SERS with digital assay revealed improved accuracy in quantitative dopamine analysis. Artificial neural network-based advanced machine learning method was exploited to improve the interpretability of bioanalytical SERS for multiple living cell responses. Finally, this dissertation provides future perspectives with different aspects to design bio-interfaced SERS devices for clinical translation, followed by guidance for SERS to become a standard analytical method that can compete with or complement existing technologies.
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    Biomimetic Transparent Nanoplasmonic Meshes by Reverse-Nanoimprinting for Bio-Interfaced Spatiotemporal Multimodal SERS Bioanalysis
    Garg, Aditya; Mejia, Elieser; Nam, Wonil; Vikesland, Peter J.; Zhou, Wei (Wiley-V C H Verlag, 2022-11)
    Multicellular systems, such as microbial biofilms and cancerous tumors, feature complex biological activities coordinated by cellular interactions mediated via different signaling and regulatory pathways, which are intrinsically heterogeneous, dynamic, and adaptive. However, due to their invasiveness or their inability to interface with native cellular networks, standard bioanalysis methods do not allow in situ spatiotemporal biochemical monitoring of multicellular systems to capture holistic spatiotemporal pictures of systems-level biology. Here, a high-throughput reverse nanoimprint lithography approach is reported to create biomimetic transparent nanoplasmonic microporous mesh (BTNMM) devices with ultrathin flexible microporous structures for spatiotemporal multimodal surface-enhanced Raman spectroscopy (SERS) measurements at the bio-interface. It is demonstrated that BTNMMs, supporting uniform and ultrasensitive SERS hotspots, can simultaneously enable spatiotemporal multimodal SERS measurements for targeted pH sensing and non-targeted molecular detection to resolve the diffusion dynamics for pH, adenine, and Rhodamine 6G molecules in agarose gel. Moreover, it is demonstrated that BTNMMs can act as multifunctional bio-interfaced SERS sensors to conduct in situ spatiotemporal pH mapping and molecular profiling of Escherichia coli biofilms. It is envisioned that the ultrasensitive multimodal SERS capability, transport permeability, and biomechanical compatibility of the BTNMMs can open exciting avenues for bio-interfaced multifunctional sensing applications both in vitro and in vivo.
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    Highly porous gold supraparticles as surface-enhanced Raman spectroscopy (SERS) substrates for sensitive detection of environmental contaminants
    Kang, Seju; Wang, Wei; Rahman, Asifur; Nam, Wonil; Zhou, Wei; Vikesland, Peter J. (Royal Society of Chemistry, 2022-11-15)
    Surface-enhanced Raman spectroscopy (SERS) has great potential as an analytical technique for environmental analyses. In this study, we fabricated highly porous gold (Au) supraparticles (i.e., ∼100 μm diameter agglomerates of primary nano-sized particles) and evaluated their applicability as SERS substrates for the sensitive detection of environmental contaminants. Facile supraparticle fabrication was achieved by evaporating a droplet containing an Au and polystyrene (PS) nanoparticle mixture on a superamphiphobic nanofilament substrate. Porous Au supraparticles were obtained through the removal of the PS phase by calcination at 500 °C. The porosity of the Au supraparticles was readily adjusted by varying the volumetric ratios of Au and PS nanoparticles. Six environmental contaminants (malachite green isothiocyanate, rhodamine B, benzenethiol, atrazine, adenine, and gene segment) were successfully adsorbed to the porous Au supraparticles, and their distinct SERS spectra were obtained. The observed linear dependence of the characteristic Raman peak intensity for each environmental contaminant on its aqueous concentration reveals the quantitative SERS detection capability by porous Au supraparticles. The limit of detection (LOD) for the six environmental contaminants ranged from ∼10 nM to ∼10 μM, which depends on analyte affinity to the porous Au supraparticles and analyte intrinsic Raman cross-sections. The porous Au supraparticles enabled multiplex SERS detection and maintained comparable SERS detection sensitivity in wastewater influent. Overall, we envision that the Au supraparticles can potentially serve as practical and sensitive SERS devices for environmental analysis applications.
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    Microporous Multiresonant Plasmonic Meshes by Hierarchical Micro-Nanoimprinting for Bio-Interfaced SERS Imaging and Nonlinear Nano-Optics
    Garg, Aditya; Mejia, Elieser; Nam, Wonil; Nie, Meitong; Wang, Wei; Vikesland, Peter J.; Zhou, Wei (Wiley-V C H Verlag, 2022-04)
    Microporous mesh plasmonic devices have the potential to combine the biocompatibility of microporous polymeric meshes with the capabilities of plasmonic nanostructures to enhance nanoscale light-matter interactions for bio-interfaced optical sensing and actuation. However, scalable integration of dense and uniformly structured plasmonic hotspot arrays with microporous polymeric meshes remains challenging due to the processing incompatibility of conventional nanofabrication methods with flexible microporous substrates. Here, scalable nanofabrication of microporous multiresonant plasmonic meshes (MMPMs) is achieved via a hierarchical micro-/nanoimprint lithography approach using dissolvable polymeric templates. It is demonstrated that MMPMs can serve as broadband nonlinear nanoplasmonic devices to generate second-harmonic generation, third-harmonic generation, and upconversion photoluminescence signals with multiresonant plasmonic enhancement under fs pulse excitation. Moreover, MMPMs are employed and explored as bio-interfaced surface-enhanced Raman spectroscopy mesh sensors to enable in situ spatiotemporal molecular profiling of bacterial biofilm activity. Microporous mesh plasmonic devices open exciting avenues for bio-interfaced optical sensing and actuation applications, such as inflammation-free epidermal sensors in conformal contact with skin, combined tissue-engineering and biosensing scaffolds for in vitro 3D cell culture models, and minimally invasive implantable probes for long-term disease diagnostics and therapeutics.
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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.