Surface Functionalization of Nanoparticles: from Lithium- Ion Battery Anode to High Refractive Index Optical Materials

Surface-functionalized nanoparticles (SF-NPs) have great potential to be used in many fields including biosensors, medicines, catalysis, environmental remediation and energy storage. This dissertation work demonstrate the development of solutions confronting specific problems in the application of nanoparticles with surface functionalization strategy. Chapter 1 presents an introduction.The electrochemical performance of silicon anode in lithium-ion battery is closely related to the surface properties of silicon nanoparticles (SiNPs). In Chapter 2, an epoxy group is attached onto the surface of SiNPs through the formation of siloxane bond by surface silanization. Electrode based on epoxy-functionalized SiNPs shows a much improved cell performance due to the improved binding system by the chemical reaction between epoxy group and poly(acrylic acid) binder and the reduced parasitic reactions with electrolyte. In Chapter3, a series of specially designed functional groups featuring ethylene oxide of different chain length terminated with an epoxy group are covalently attached to SiNPs by surface hydrosilylation. When employed as active materials for Si-graphite electrode, the surface-functionalized SiNPs improve cell performance with enhanced Li+ transport, stronger binding system and improved anode surface stability.A feasible way to make processable high refractive index (RI) optical materials is to introduce high RI inorganic nanofillers into the processable polymer matrix. A strong interaction between the two components is desired to prevent aggregation of nanoparticles in polymer. In Chapter 4, sulfur-containing polymer brush-grafted titanium dioxide (TiO2) nanoparticles (hairy TiO2 NPs) are made by surface-initiated atom transfer radical polymerization (SI-ATRP) .The incorporation of sulfur atom, which has high molar refraction, into side chain of vinyl monomers increases the intrinsic RI of the grafted polymer chains. The hairy TiO2 NPs, featuring tunable ratio between grafted polymers and inorganic core, good dispersion and processability, have great potential to be used alone or to be used as building block in processable high RI nanocomposites.Chapter 5 presents surface functionalization of SiNPs by surface-initiated “living”/controlled radical polymerization (SI-LRP). Polymer-grafted SiNPs show good stability in common solvents and are expected to be applied in many practical fields including sustainable energy storage, semiconductors and optical industry.A conclusion and future perspective are given in Chapter 6

Achieving the Heisenberg limit in quantum metrology using quantum error correction

Quantum metrology has many important applications in science and technology, ranging from frequency spectroscopy to gravitational wave detection. Quantum mechanics imposes a fundamental limit on measurement precision, called the Heisenberg limit, which can be achieved for noiseless quantum systems, but is not achievable in general for systems subject to noise. Here we study how measurement precision can be enhanced through quantum error correction, a general method for protecting a quantum system from the damaging effects of noise. We find a necessary and sufficient condition for achieving the Heisenberg limit using quantum probes subject to Markovian noise, assuming that noiseless ancilla systems are available, and that fast, accurate quantum processing can be performed. When the sufficient condition is satisfied, a quantum error-correcting code can be constructed which suppresses the noise without obscuring the signal; the optimal code, achieving the best possible precision, can be found by solving a semidefinite program.Comment: 16 pages, 2 figures, see also arXiv:1704.0628

Development of a Pavement Evaluation Tool Using Aerial Imagery and Deep Learning

This paper presents the research results of using Google Earth imagery for visual condition surveying of highway pavement in the United States. A screenshot tool is developed to automatically track the highway for collecting end-to-end images and Global Position System (GPS). A highway segmentation tool based on a deep convolutional neural network (DCNN) is developed to segment the collected highway images into the predefined object categories, where the cracks are identified and labeled in each small patch of the overlapping assembled label-image prediction. Then, the longitudinal cracks and transverse cracks are detected using the x-gradient and y-gradient from the Sobel operator, and the developed pavement evaluation tool rates the longitudinal cracking in (linear feet per 100 ft. station) and transverse cracking in number per -Station (100 ft. station), which can be visualized in ArcGIS Online. Experiments were conducted on Interstate 43 (I-43) in Milwaukee County with pavement in both defective and sound visual conditions. Experimental results showed the patch-wise highway segmentation in Google Earth imagery from the DCNN model has as precise pixel accuracy as the U-net-based pixelwise crack/noncrack classifier. Compared to the manually crafted label image in the experimental area, the rated longitudinal cracking has an average error of overrating 20%, while transverse cracking has an average error of underrating 7%. This research project contributes to visual pavement condition surveying methodology with the free-to-access Google Earth imagery, which is a feasible, cost-effective option for accurately rating and geographically visualizing both project-level and network-level pavement

Building and Infrastructure Defect Detection and Visualization Using Drone and Deep Learning Technologies

This paper presents an accurate and stable method for object and defect detection and visualization on building and infrastructural facilities. This method uses drones and cameras to collect three- dimensional (3D) point clouds via photogrammetry, and uses orthographic or arbitrary views of the target objects to generate the feature images of points’ spectral, elevation, and normal features. U-Net is implemented in the pixelwise segmentation for object and defect detection using multiple feature images. This method was validated on four applications, including on-site path detection, pavement cracking detection, highway slope detection, and building facade window detection. The comparative experimental results confirmed that U-Net with multiple features has a better pixelwise segmentation performance than separately using each single feature. The developed method can implement object and defect detection with different shapes, including striped objects, thin objects, recurring and regularly shaped objects, and bulky objects, which will improve the accuracy and efficiency of inspection, assessment, and management of buildings and infrastructural facilities

Scan4Façade: Automated As-Is Façade Modeling of Historic High-Rise Buildings Using Drones and AI

This paper presents an automated as-is façade modeling method for existing and historic high-rise buildings, named Scan4Façade. To begin with, a camera drone with a spiral path is employed to capture building exterior images, and photogrammetry is used to conduct three-dimensional (3D) reconstruction and create mesh models for the scanned building façades. High-resolution façade orthoimages are then generated from mesh models and pixelwise segmented by an artificial intelligence (AI) model named U-net. A combined data augmentation strategy, including random flipping, rotation, resizing, perspective transformation, and color adjustment, is proposed for model training with a limited number of labels. As a result, the U-net achieves an average pixel accuracy of 0.9696 and a mean intersection over union of 0.9063 in testing. Then, the developed twoStagesClustering algorithm, with a two-round shape clustering and a two-round coordinates clustering, is used to precisely extract façade elements’ dimensions and coordinates from façade orthoimages and pixelwise label. In testing with the Michigan Central Station (office tower), a historic high-rise building, the developed algorithm achieves an accuracy of 99.77% in window extraction. In addition, the extracted façade geometric information and element types are transformed into AutoCAD command and script files to create CAD drawings without manual interaction. Experimental results also show that the proposed Scan4Façade method can provide clear and accurate information to assist BIM feature creation in Revit. Future research recommendations are also stated in this paper