Size Controlled Metal Oxide Nanoparticles:Synthesis, Characterization, and Application to Catalysis

The research in this dissertation focuses on the synthesis of size-controlled metal oxide nanoclusters (\u3c 10 nm) on amorphous silica and their catalytic performance in thermal degradation of chlorinated benzenes with regard to the cluster size effect. Furthermore, with the concern that metal can condense as the nano-size nuclei core for particle growth in combustion process, a flow reactor was built to investigate the effect of metal oxide nanoparticles on the formation of soot in fuel-rich combustion. The synthesis of copper oxide nanoclusters was carried by calcination of silica impregnated with dendrimer-metal complexes. The 4th generation poly(propylene imine) dendrimer DAB-Am32 was used in this template-based method. The sizes of copper oxide nanoclusters were exquisitely controlled in the range of 1-5 nm with narrow size distribution by changing the stoichiometric ratio of metal ion to the terminal primary amines of dendrimer, the equivalent metal oxide loading on surface, and the impregnation procedure. XANES and XPS studies revealed that CuO was the dominant component of copper oxide nanoclusters. This method was also experimentally proven to be valid in the preparation of other metal oxide nanoparticles, e.g., Ni and Fe, and with other oxide substrates, e.g., titanium oxide. Chlorinated benzenes were selected as the model compound for studying the activity of metal (Cu and Fe) oxide catalysts with regard to their cluster sizes. Compared to the surrogate of coarse metal oxide samples, which was prepared by incipient wetness method, their nanosize analogues showed superior catalytic activity on the conversion of chlorinated benzenes under both pyrolytic and oxidative thermal condition. Furthermore, such catalytic size effect was also observed on the selectivity of products yields. Sooting combustion was performed using a two-zone flow reactor with precise control on experimental parameters. Gas suspended metal oxide nanoparticles were generated by burning off the organic backbone of the dendrimer-metal complexes in zone 1 and immediately transferred to zone 2, where the hydrocarbon combustion occurred. TEM results of the particulate sample collected at the outlet of reactor indicated that metal oxide nanoparticles promoted soot formation. GC/MS analysis of the extracted organic materials from soot samples suggested the formation of PAH was also promoted by metal addition as well

Spin-valley qubit in nanostructures of monolayer semiconductors: Optical control and hyperfine interaction

We investigate the optical control possibilities of spin-valley qubit carried by single electrons localized in nanostructures of monolayer TMDs, including small quantum dots formed by lateral heterojunction and charged impurities. The quantum controls are discussed when the confinement induces valley hybridization and when the valley hybridization is absent. We show that the bulk valley and spin optical selection rules can be inherited in different forms in the two scenarios, both of which allow the definition of spin-valley qubit with desired optical controllability. We also investigate nuclear spin induced decoherence and quantum control of electron-nuclear spin entanglement via intervalley terms of the hyperfine interaction. Optically controlled two-qubit operations in a single quantum dot are discussed.Comment: 17pages, 10 figure

DeepPOSE: Detecting GPS Spoofing Attack Via Deep Recurrent Neural Network

The Global Positioning System (GPS) has become a foundation for most location-based services and navigation systems, such as autonomous vehicles, drones, ships, and wearable devices. However, it is a challenge to verify if the reported geographic locations are valid due to various GPS spoofing tools. Pervasive tools, such as Fake GPS, Lockito, and software-defined radio, enable ordinary users to hijack and report fake GPS coordinates and cheat the monitoring server without being detected. Furthermore, it is also a challenge to get accurate sensor readings on mobile devices because of the high noise level introduced by commercial motion sensors. To this end, we propose DeepPOSE, a deep learning model, to address the noise introduced in sensor readings and detect GPS spoofing attacks on mobile platforms. Our design uses a convolutional and recurrent neural network to reduce the noise, to recover a vehicle\u27s real-time trajectory from multiple sensor inputs. We further propose a novel scheme to map the constructed trajectory from sensor readings onto the Google map, to smartly eliminate the accumulation of errors on the trajectory estimation. The reconstructed trajectory from sensors is then used to detect the GPS spoofing attack. Compared with the existing method, the proposed approach demonstrates a significantly higher degree of accuracy for detecting GPS spoofing attacks

A Channel State Information Based Virtual MAC Spoofing Detector

Physical layer security has attracted lots of attention with the expansion of wireless devices to the edge networks in recent years. Due to limited authentication mechanisms, MAC spoofing attack, also known as the identity attack, threatens wireless systems. In this paper, we study a new type of MAC spoofing attack, the virtual MAC spoofing attack, in a tight environment with strong spatial similarities, which can create multiple counterfeits entities powered by the virtualization technologies to interrupt regular services. We develop a system to effectively detect such virtual MAC spoofing attacks via the deep learning method as a countermeasure. A deep convolutional neural network is constructed to analyze signal level information extracted from Channel State Information (CSI) between the communication peers to provide additional authentication protection at the physical layer. A significant merit of the proposed detection system is that this system can distinguish two different devices even at the same location, which was not well addressed by the existing approaches. Our extensive experimental results demonstrate the effectiveness of the system with an average detection accuracy of 95%, even when devices are co-located

Stabilization and current-induced motion of antiskyrmion in the presence of anisotropic Dzyaloshinskii-Moriya interaction

Topological defects in magnetism have attracted great attention due to fundamental research interests and potential novel spintronics applications. Rich examples of topological defects can be found in nanoscale non-uniform spin textures, such as monopoles, domain walls, vortices, and skyrmions. Recently, skyrmions stabilized by the Dzyaloshinskii-Moriya interaction have been studied extensively. However, the stabilization of antiskyrmions is less straightforward. Here, using numerical simulations we demonstrate that antiskyrmions can be a stable spin configuration in the presence of anisotropic Dzyaloshinskii-Moriya interaction. We find current-driven antiskyrmion motion that has a transverse component, namely antiskyrmion Hall effect. The antiskyrmion gyroconstant is opposite to that for skyrmion, which allows the current-driven propagation of coupled skyrmion-antiskyrmion pairs without apparent skyrmion Hall effect. The antiskyrmion Hall angle strongly depends on the current direction, and a zero antiskyrmion Hall angle can be achieved at a critic current direction. These results open up possibilities to tailor the spin topology in nanoscale magnetism, which may be useful in the emerging field of skyrmionics.Comment: 31 pages, 6 figures, to appear in Physical Review

Camouflaged Poisoning Attack on Graph Neural Networks

Graph neural networks (GNNs) have enabled the automation of many web applications that entail node classification on graphs, such as scam detection in social media and event prediction in service networks. Nevertheless, recent studies revealed that the GNNs are vulnerable to adversarial attacks, where feeding GNNs with poisoned data at training time can lead them to yield catastrophically devastative test accuracy. This finding heats up the frontier of attacks and defenses against GNNs. However, the prior studies mainly posit that the adversaries can enjoy free access to manipulate the original graph, while obtaining such access could be too costly in practice. To fill this gap, we propose a novel attacking paradigm, named Generative Adversarial Fake Node Camouflaging (GAFNC), with its crux lying in crafting a set of fake nodes in a generative-adversarial regime. These nodes carry camouflaged malicious features and can poison the victim GNN by passing their malicious messages to the original graph via learned topological structures, such that they 1) maximize the devastation of classification accuracy (i.e., global attack) or 2) enforce the victim GNN to misclassify a targeted node set into prescribed classes (i.e., target attack). We benchmark our experiments on four real-world graph datasets, and the results substantiate the viability, effectiveness, and stealthiness of our proposed poisoning attack approach. Code is released in github.com/chao92/GAFNC

View Synthesis With Scene Recognition for Cross-View Image Localization

Image-based localization has been widely used for autonomous vehicles, robotics, augmented reality, etc., and this is carried out by matching a query image taken from a cell phone or vehicle dashcam to a large scale of geo-tagged reference images, such as satellite/aerial images or Google Street Views. However, the problem remains challenging due to the inconsistency between the query images and the large-scale reference datasets regarding various light and weather conditions. To tackle this issue, this work proposes a novel view synthesis framework equipped with deep generative models, which can merge the unique features from the outdated reference dataset with features from the images containing seasonal changes. Our design features a unique scheme to ensure that the synthesized images contain the important features from both reference and patch images, covering seasonable features and minimizing the gap for the image-based localization tasks. The performance evaluation shows that the proposed framework can synthesize the views in various weather and lighting conditions