Ultrasensitive surface enhanced Raman scattering nanomotors : for location predicable biochemical detection, single-cell bioanalysis, and controllable biochemical release and real-time detection

Localized surface plasmon resonance resulting from the concerted oscillations of conduction-band electrons in noble-metal (Au, Ag) nanostructures greatly induces enhanced electric ([italic E]) fields in confined nanoscale locations, such as on the tips of nanorods or in the junctions of nanodimers. These [italic E]-field enhanced locations are called hot spots. In the vicinity of hot spots, Raman scattering spectra of biochemicals can be substantially amplified with an [italic E]⁴ dependence due to the [italic E]-field enhancement of both the incident light and Raman spectra. This is called surface enhanced Raman scattering (SERS). SERS is known for its high sensitivity in providing fingerprint vibrational information of molecules. It has triggered intense interest because of its potential applications for label-free and multiplex biochemical detection relevant to medical, environmental and defense purposes. However, the tremendous potential of SERS for ultrasensitive detection has still not materialized because of four major obstacles: (1) it is extremely difficult to obtain a large number of hotspots for sensitive and reproducible detection due to the stringent requirement of hot spots of only a few nanometers; (2) it is arduous to achieve ultrasensitivity for the detection of a single/few molecules; (3) it is challenging to assemble the hot-spots at designated positions for location predicable sensing; and (4) it is even more difficult to change the state-of-the-art static/passive sensing schemes into dynamic/robotized schemes and also to incorporate multi-functionality into a single SERS nanostructure. In this research, we addressed the aforementioned problems by rational design, fabrication and robotization of ultrasensitive SERS nanomotor sensors. A nanomotor sensor consists of a tri-layer structure with a three-segment Ag/Ni/Ag nanorod as the core, a thin layer of silica in the center, and uniformly distributed Ag nanoparticles as the outer layer. The inner metallic nanorod core is the key structure in realizing the concept of the robotization of nanosensors, which can be electrically polarized and thus efficiently manipulated by electric tweezers. The presence of the Ni segment in the metallic nanowire core also allows manipulation and assembling by magnetic interactions. The central silica layer provides a supporting substrate for the synthesis of the Ag nanoparticles and separates the Ag nanoparticles from the metallic nanorod core to eliminate the effect of plasmonic quenching. Finally, the outermost layer made of Ag nanoparticles with optimized sizes and junctions provides a large number of hot spots (~1200/μm²) for ultrasensitive SERS detection with single molecule sensitivity and an enhancement factor (EF) of 1.1×10¹⁰. Moreover, two additional SERS enhancement mechanisms were investigated, i.e., the optical management with nanophotonic devices and the near field effect, which can readily increase the EF by 10 and 2 times, respectively, to at least 10¹¹. Finally, three applications of the SERS nanomotor sensors have been demonstrated: (1) the ultrasensitive SERS nanomotors were transported and assembled into a 3×3 array for location predicable sensing of multiplex molecules; (2) ultrasensitive SERS nanomotors were transported to individual living cells amidst many cells for single-cell bioanalysis; and (3) the SERS nanomotor sensors can be controlled to rotate by the electric tweezers for tunable biochemical release and detection. This research, exploring robotized nanosensors by integrating SERS and NEMS, is innovative in both material design and device concept, which could inspire a new device scheme for various bio-relevant applications.Materials Science and Engineerin

Motif-informed analysis of phenotype heterogeneity in cancer

The landscape of cancer genomics harbors a wealth of DNA motifs, whose thorough analysis and integration provide a pivotal method to decipher the complex molecular interactions underlying cancer. This dissertation delineates novel computational methodologies for robust DNA motif analysis and data integration, aiming to elucidate the implications of DNA motifs on cancer heterogeneity and clinical outcomes. Chapter 1 lays the groundwork by showing the significance of DNA motifs in the genomic framework and delineating the current biomarkers in cancer. It highlights the opportunity that DNA motif analysis presents in unveiling a nuanced understanding of genomic interactions. It also indicates the motivations and specific aims of the study of both DNA motif quantification and co-localization analysis. In Chapter 2, a foundational marker for quantifying the prevalence of DNA repetitive motifs, termed as “Non-B DNA Burden”, is introduced. A user-centric platform is also developed to facilitate the efficient computation and visualization of this metric across various genomic scales. Together, they are offering a novel perspective for analyzing DNA motif heterogeneity. Transitioning to Chapter 3, the focus evolves toward an integrated marker approach. By integrating the prevalence analysis of DNA motifs in conjunction with the frequency of co-localized mutations, novel markers mlTNB (mutation-localized total non-B burden) and nbTMB (non-B informed tumor mutation burden) are proposed. Their potential in predicting cancer prognosis and treatment responses is specifically explored. Chapter 4 broadens the analytical foundation by defining MoCoLo (Motif Co-Localization), a robust statistical framework for testing multi-modal DNA motif co-localization. Through this framework, we are able to explore the complex interplay of genomic features and provide a methodical approach to investigate their co-localization in a multi-modal data integration context. Case studies are employed to showcase the utility of MoCoLo in examining the co-localization of genomic features, thus facilitating the understanding of genomic interactions that are pivotal to cancer biology. Chapter 5 synthesizes the findings from the preceding explorations, outlining the contributions of the developed methodologies to the field of cancer genomics and bioinformatics. It demonstrates the potential impact of DNA motif analysis and data integration on understanding phenotype heterogeneity in cancer and shows the prospective avenues it provides for impactful future research. Overall, this work is structured to contribute to the bioinformatics community by weaving together innovative tools and analyses focused on DNA motif analysis and data integration. It strives to pave a beneficial way forward to a deeper understanding of the cancer genome, thereby enhancing potential diagnostic and therapeutic strategies.Cellular and Molecular Biolog

Edge Shear Flows and Particle Transport near the Density Limit in the HL-2A Tokamak

Edge shear flow and its effect on regulating turbulent transport have long been suspected to play an important role in plasmas operating near the Greenwald density limit $n_G$. In this study, equilibrium profiles as well as the turbulent particle flux and Reynolds stress across the separatrix in the HL-2A tokamak are examined as $n_G$ is approached in ohmic L-mode discharges. As the normalized line-averaged density $\bar{n}_e/n_G$ is raised, the shearing rate of the mean poloidal flow $\omega_{\rm sh}$ drops, and the turbulent drive for the low-frequency zonal flow (the Reynolds power $\mathcal{P}_{Re}$) collapses. Correspondingly, the turbulent particle transport increases drastically with increasing collision rates. The geodesic acoustic modes (GAMs) gain more energy from the ambient turbulence at higher densities, but have smaller shearing rate than low-frequency zonal flows. The increased density also introduces decreased adiabaticity which not only enhances the particle transport but is also related to a reduction in the eddy-tilting and the Reynolds power. Both effects may lead to the cooling of edge plasmas and therefore the onset of MHD instabilities that limit the plasma density

Introduction to half-metallic Heusler alloys: Electronic Structure and Magnetic Properties

Intermetallic Heusler alloys are amongst the most attractive half-metallic systems due to the high Curie temperatures and the structural similarity to the binary semiconductors. In this review we present an overview of the basic electronic and magnetic properties of both Heusler families: the so-called half-Heusler alloys like NiMnSb and the the full-Heusler alloys like Co$_2$MnGe. \textit{Ab-initio} results suggest that both the electronic and magnetic properties in these compounds are intrinsically related to the appearance of the minority-spin gap. The total spin magnetic moment $M_t$ scales linearly with the number of the valence electrons $Z_t$, such that $M_t=Z_t-24$ for the full-Heusler and $M_t=Z_t-18$ for the half-Heusler alloys, thus opening the way to engineer new half-metallic alloys with the desired magnetic properties.Comment: 28 pages, submitted for a special issue of 'Journal of Physics D: Applied Physics' on Heusler alloy

Application of reactive melt extrusion for bioavailability enhancement and modified drug release

Hot melt extrusion (HME) has been widely applied to prepare amorphous solid dispersions (ASD) to improve the oral bioavailability of BCS Class II and Class IV compounds by increasing their kinetic solubility and dissolution rate. During the HME process, drug, polymer and other excipients are introduced into the barrel at different temperature setting and feed rates. The intermeshing screws mix and melt all materials using heat and an intense mechanical shearing force to achieve distributive and dispersive mixing and excellent homogeneity. The molecular level mixing allows close contact between API and excipients at high frequencies, which provide favorable environment to build drug-excipient intermolecular interactions to improve the physicochemical properties of ASD. Even though there are extensive reports about the pharmaceutical application of HME, most of the studies have been restricted to the manufacture of drug delivery systems where no clearly defined molecular level interaction are produced. Since the extrusion process is a high temperature and aggressive molecular level mixing process, lot of interactions would occur during the extrusion process, such as the ionic interaction, hydrogen bonding, pi-pi interaction, Van der Walls forces and lipophilic-lipophilic interactions. The rational design interactions between drug and excipients during the HME process would provide an inspiring strategy to overcome the drawback of HME, such as the thermal degradation of drug, poor physical stability of drug during the storage time or dissolution process. For ASD development, the polymer carriers play a critical role in stabilizing the drug amorphous state. Polymer selection to prepare the ASDs is largely empirical. There is a need for rational polymer selection, enabling design of stable amorphous solid dispersion. Drug-polymer interactions have been observed to improve the physical stability of ASDs. Supramolecular synthon approach has been applied to design cocrystal with adjusting physicochemical properties. What’s more, supramolecular synthon approach has been exploited to design ASD with exceptional physical stability. Based on all those non-covalent interactions, it is possible to achieve the in-situ modification of solid forms of active pharmaceutical ingredients by mechanochemistry using extrusion process, without changing the pharmacology of the API. The major goal of this research is to explore rational design interaction between drug and excipients during the HME process to prepare salt, polyelectronic complexes, nanocomposites, cocrystal and coamorphous to improve the oral bioavailability of poorly water-soluble drugs and adjusting drug release rate. In Chapter 1, we reviewed the most commonly used methods for characterization of ASDs both in solid state or in aqueous media. The advantage and disadvantage of each method is briefly summarized. All methods are divided into three different categories: microscopic and surface analysis methods, thermal analysis methods and spectroscopic methods. The latest characterization techniques are also introduced. Last, we discuss how these methods are applied at different stages in the ASDs product development life cycle. In Chapter 2, we investigate the reaction between naproxen and meglumine at elevated temperature with different molar ratio and study the impact of this reaction on the physical stabilities and in vitro drug-release properties of melt-extrudated naproxen amorphous solid dispersion. In Chapter 3, we use reactive melt extrusion to prepare sustained release lidocaine polyelectrolyte complex. In this study, the influence of the drug form (freebase vs. hydrochloride salt) on lidocaine-Eudragit L100-55 interactions, physical stability, and dissolution properties of extrudates was investigated. In Chapter 4, we prepare exfoliated montmorillonite-Eudragit RS nanocomposites using reactive melt extrusion and investigate the influence of clay loading, clay types on clay-polymer interactions and drug release properties. The clays are used as the filler material with Eudragit RS at different concentration and theophylline was the model compound. The resulting structure of the nanocomposites were characterized using TEM and XRPD. The hygroscopicity of the nanocomposites was investigated using DVS. The effect of the interfacial interaction between the polymer and the clay sheet, the clay loading as well as the clay type on the drug release behavior were further studied by the dissolution testing.Pharmaceutical Science

Standard cell optimization and physical design in advanced technology nodes

Integrated circuits (ICs) are at the heart of modern electronics, which rely heavily on the state-of-the-art semiconductor manufacturing technology. The key to pushing forward semiconductor technology is IC feature-size miniaturization. However, this brings ever-increasing design complexities and manufacturing challenges to the $340 billion semiconductor industry. The manufacturing of two-dimensional layout on high-density metal layers depends on complex design-for-manufacturing techniques and sophisticated empirical optimizations, which introduces huge amounts of turnaround time and yield loss in advanced technology nodes. Our study reveals that unidirectional layout design can significantly reduce the manufacturing complexities and improve the yield, which is becoming increasingly adopted in semiconductor industry [61, 89]. The lithography printing of unidirectional layout can be tightly controlled using advanced patterning techniques, such as self-aligned double and quadruple patterning. Despite the manufacturing benefits, unidirectional layout leads to more restrictive solution space and brings significant impacts on the IC design automation ow for routing closure. Notably, unidirectional routing limits the standard cell pin accessibility, which further exacerbates the resource competitions during routing. Moreover, for post-routing optimization, traditional redundant-via insertion has become obsolete under unidirectional routing style, which makes the yield enhancement task extremely challenging. Regardless of complex multiple patterning and design-for-manufacturing approaches, mask optimization through resolution enhancement techniques remains as the key strategy to improve the yield of the semiconductor manufacturing processes. Among them, Sub-Resolution Assist Feature (SRAF) generation is a very important method to improve lithographic process windows. Model-based SRAF generation has been widely used to achieve high accuracy but it is time-consuming and hard to obtain consistent SRAFs. This dissertation proposes novel CAD algorithms and methodologies for standard cell optimization and physical design in advanced technology nodes, which ultimately reduces the design cycle and manufacturing cost of IC design. First, a standard cell pin access optimization engine is proposed to evaluate the pin accessibility of a given standard cell library. We further propose novel pin access planning techniques and concurrent pin access optimizations to efficiently resolve the routing resource competitions, which generates much better routing solutions than state-of-the-art, manufacturing-friendly routers. To systematically improve the manufacturing yield in the post-routing stage, a global optimization engine has been introduced for redundant local-loop insertion considering advanced manufacturing constraints. Finally, we propose the first machine learning-based framework for fast yet consistent SRAF generation with the high quality of results.Electrical and Computer Engineerin

Data cleaning and knowledge discovery in process data

This dissertation presents several methods for overcoming the Big Data challenges, with an emphasis on data cleaning and knowledge discovery in process data. Data cleaning and knowledge discovery is chosen as a main research area here due to its importance from both theoretical and practical points of view. Theoretical background and recent developments of data cleaning methods are reviewed from four aspects: missing data imputation, outlier detection, noise removal and time delay estimation. Moreover, the impact of contaminated data on model performance and corresponding improvement obtained by data cleaning methods are analyzed through both simulated and industrial case studies. The results provide a starting point for further advanced methodology development. It is hard to find a universally applicable method for data cleaning since every data set may have its own distinctive features. Thus, we have to customize available methods so that the quality of the data set is guaranteed. An integrated data cleaning scheme is proposed, which incorporates model building and performance evaluation, to provide guidance in tuning the parameters of data cleaning methods and prevent over-cleaning. A case study based on industrial data has been used to verify the feasibility and effectiveness of the proposed new method, during which a partial least squares (PLS) model was built and three univariate data cleaning procedures is tested. A time series Kalman filter (TSKF) is proposed that successfully handles outlier detection in dynamic systems, where normal process changes often mask the existence of outliers. The TSKF method combines a time series model fitting procedure with a modified Kalman filter to deal with additive outlier (AO) and innovational outlier (IO) detection problems in dynamic process data set. A comparative analysis of TSKF and available methods is performed on simulated and real chemical plant data. Root cause diagnosis of plant-wide oscillations, as a concrete example of data cleaning and knowledge discovery in the process data, is provided. Plant-wide oscillations can negatively influence the overall control performance of the process and the detection results are often affected by noise at different frequency ranges. To address such a problem, an information transfer method combining spectral envelope algorithm with spectral transfer entropy is proposed to detect and diagnose such oscillations within a specific frequency range, mitigating the effects from measurement noise. The feasibility and effectiveness of the proposed method are verified and compared with available methods through both simulated and industrial case studies.Chemical Engineerin

Fabrication and integration of metasurfaces and metagratings into organic photodetectors and light emitters

The field of optics has been transformed with the advent of high precision nanofabrication techniques that can be used fabricate subwavelength features in the optical regime. Advanced nanofabrication can be used to create engineered materials with optical properties unlike those found in nature and are impossible to achieve using conventional optics. Metasurfaces and metagratings are examples of these new types of engineered materials with unprecedented control over light. These metasurfaces and metagratings are composed of resonators at the nanoscale composed of either plasmonic or dielectric materials. The careful arrangement of these resonators can be used to create interfaces that can steer light in arbitrary directions, control the polarization and manipulate the phase. While there have been many studies into the fundamental design, fabrication, and characterization of these new engineered optical materials, there is relatively fewer work on integrating and implementing these materials into practical optical devices. This work will therefore be focused on the fabrication and integration of metasurfaces and metagratings into organic photodetectors and organic light emitters. In the first part of this work, we integrate a metasurface into an organic photodetector. Ultrathin flexible light sensors have many interesting applications but have drawbacks in their efficiencies because the light absorbing material is too thin to absorb a large degree of the incident light. For this application, the addition of a metasurface can be used to enhance the absorption and make the device appear optical thicker without adding too much additional thickness. We experimentally demonstrate metasurface-enhanced photoresponse in organic photodetectors. We have designed and integrated a metasurface with broadband functionality into an organic photodetector, with the goal of significantly increasing the absorption of light and generated photocurrent from 560 nm up to 690 nm. Our results show large gains in responsivity from 1.5X to 2X between 560 nm and 690 nm. Secondly, we investigate the integration of metagratings into organic light emitting devices with the goal of enhancing emission. Organic light emitting diodes are a common device for mobile screens and televisions but suffer from to a relatively low external quantum efficiency due to generated light being trapped in waveguide modes in the high refractive index organic materials. We incorporated several different types of metagrating to enhance the output light coupling. These included gratings based on a square lattice, 1D periodic lines, and quasicrystal patterns. Discussed are the key parameters for the behavior of the enhancement, such as spacing of the grating from the backside reflector, the periodicity of the grating, and the type of grating. In addition, we study the angular emission pattern and how it differs with differing grating parameters, and also study the polarization dependence of the three patterns as wellElectrical and Computer Engineerin

Pore scale study of gas sorption and transport in shale nanopore systems

Shale gas production accounts for about 70% of the total natural gas production in the US. Yet it remains a nontrivial task to characterize the petrophysical properties of shale core samples either by experimental analysis or numerical simulations. Shale matrix has low porosity and permeability resulting from nanometer-scale pore sizes. Surface properties of shale can be quite inhomogeneous arising from complex mineralogy and diagenesis. Heterogeneous morphology and topology of the pore structure poses significant challenges on understanding fluid distribution and flow capacity. Pore scale simulations provide insight into the fundamental mechanisms of thermodynamics and hydrodynamics in tight porous materials, and can supplement experimental characterization of shale petrophysical properties (e.g. absolute/relative permeability, capillary pressure curves). However, challenges exist in creating representative pore structures tailored for specific simulation tools, incorporating the appropriate and relevant physics for the problems to be simulated, and interpreting, calibrating, or validating the simulation results. In this work, we used two types of pore scale simulation tools, namely pore network modeling (PNM) and lattice Boltzmann method (LBM), to study gas adsorption/desorption and transport behavior in shale matrix. For the first part of the work, a dual-scale PNM was integrated with lattice density functional theory (LDFT) to study nitrogen adsorption/desorption in mesoporous materials with pore sizes smaller than 200 nm. Critical pore structure parameters (i.e. porosity, pore size distribution, and pore throat connectivity) were characterized by calibrating the simulated nitrogen sorption isotherms to experimental results, and were then used to construct PNMs to study supercritical methane transport. We found that the pore structure characterization results were nonunique and highly dependent on the assumed pore shape. Scanning electron microscope (SEM) images were used to further constrain the description of pore shapes. Advection and diffusion of methane at reservoir conditions were simulated and compared, and suggestions were made regarding the choice of representative pore shape in PNMs for single phase advection/diffusion calculations. We next used LBM to study two-phase thermodynamic and hydrodynamic problems in nanopore systems in shale. Both 2D and 3D LBM models were developed with consideration of mesoscale fluid-fluid and solid-fluid interactions to model gas adsorption in complex geometries, and phase separation occurs automatically without the need to track the interface. This overcomes the pore shape deficiency of PNMs in cases where nanoporous media reconstruction exists. LBM models were then calibrated to LDFT and validated against experimental adsorption data for both subcritical and supercritical gases for the first time. We studied and compared nitrogen sorption hysteresis in two model nanopore system reconstructions representing the interparticle and intraparticle pores in shale. As another example of many possible applications of our developed model, we studied water adsorption and condensation in a reconstructed clay pore structure based on SEM image analysis, and explored the effect of surface wettability on adsorbed/condensed water distribution and connectivity. Supercritical methane flow simulations with the existence of condensed water were conducted using a 3D hydrodynamic LBM model that considers nanoscale flow physics for high Knudsen number flow. The relative permeability of methane as a function of water saturation and surface wettability was calculated and compared to available experimental data measured on geosynthetic clay liners. We demonstrated the wide applicability of our model and suggested future applicationsPetroleum and Geosystems Engineerin