Browsing by Author "Zhang, Xiao"

The chemical industry depends on heterogeneous thermocatalytic processes to satisfy the ever-increasing demand for fuels and fertilizers. High temperatures and high pressures are generally required to accelerate chemical transformations and operate practical rates. These harsh conditions, however, lead to huge energy consumption and other side effects, such as the lifetime of catalysts and parasitic formation of by-products. Light is used as an alternative energy input to drive chemical reactions on semiconducting photocatalysts, but the slow reaction rates and insufficient control of product selectivity hinder wide adaptation of photocatalysis. Plasmonic metal nanoparticles have been recently proposed as a new type of catalysts with photoactivities. As already been widely used in thermocatalytic reactions, the strong light absorption capability from excitation of localized surface plasmon resonance (LSPR) of plasmonic catalysts could combine light and thermal energy to work cooperatively in enhancing rates of chemical reactions. This dissertation summarizes our efforts aiming to design plasmonic catalysts with high efficiency and high product selectivity. The catalytic properties of synthesized rhodium (Rh) and ruthenium (Ru) catalysts are investigated in two model reactions, carbon dioxide (CO2) hydrogenation and ammonia (NH3) synthesis.

Chapter 2 describes the development of slow-injection polyol methods to synthesize monodispersed Rh nanocubes with tunable size and resonant energy. The wide size tunability of slow-injection methods allows for the red-shift of resonant wavelength of small Rh nanostructures, which are in the deep ultraviolet (UV) region, to more accessible and practical near-UV and visible regions by increasing the size of Rh nanocubes.

Chapter 3 focuses on the product selectivity of plasmonic Rh nanocubes in CO2 hydrogenation. Rh nanocubes supported on aluminum oxide (Al2O3) nanoparticles equally produce methane (CH4) and carbon monoxide (CO) in pure thermal conditions. Under illumination of UV and blue light, the rate of CH4 production is significantly enhanced, and almost exclusive CH4 production is observed. This photo-selectivity can be attributed to selective activation of specific reaction intermediate by photo-generated hot electrons among competing reaction pathways.

Chapter 4 describes the effects of catalyst support and morphology of plasmonic Rh nanostructures on the catalytic activities in plasmon-enhanced CO2 hydrogenation. Significant improvements of reaction rates are observed by switching to reducible titanium oxide (TiO2) support and shrinking the size of Rh nanostructures. The enhancement of reaction rates by light can be partially attributed to local heating of catalyst bed.

Chapter 5 focuses on the catalytic activities of Ru-based catalysts for NH3 synthesis under light illumination. Photo-enhanced NH3 production, which highly depends on the size, support, and promoter of catalysts, is observed.

Chapter 6 discusses conclusion and future directions of this project. Molecular level insights of plasmon-enhanced catalysis are highly desired for both fundamental research and practical applications.

IP Anycast, as a vital routing technique, can distribute user requests to different servers with the same IP worldwide. It can improve large-scale distributed systems performance and load balance. Nonetheless, all the sites in the anycast-based system have identical IP addresses, which makes it challenging to control the system’s catchment (which site the user should go to) and results in anycast performance inefficiency.

In this thesis, we introduce two approaches to optimize the performance of IP anycast, proactively and passively. The first approach-AnyOpt, managed to build a prediction model to predict the catchment site of the user with controlled experiments and measurements with the sites. Using AnyOpt, a network operator can find a subset of anycast sites that minimizes client latency. In an experiment using 15 sites, each peering with one of six transit providers, AnyOpt predicted site catchments of 15 300 clients with 94.7% accuracy and client RTTs with a mean error of 4.6%. AnyOpt identified a subset of 12 sites, announcing to which lowers the mean RTT to clients by 33 ms compared to a greedy approach that enables the same number of sites with the lowest average unicast latency.

The second approach-regional anycast, is an approach that we found to have already been implemented by two large CDNs (Edgio and Imperva). In regional anycast, a CDN divides its content-hosting sites into different geographic regions, announces a distinct IP anycast prefix from each region, and uses DNS and IP-geolocation to direct a client to a CDN site in the same geographic area. We aim to understand how a regional anycast CDN partitions its sites and maps its customers’ clients, and how a regional anycast CDN performs compared to its global anycast counterpart. We study the deployment strategies and the performance of two CDNs (Edgio and Imperva) that currently deploy regional IP anycast. We find that both Edgio and Imperva partition their sites and clients following continent or country borders. In addition, we compare the client latency distribution in Imperva’s regional anycast CDN with that in its similar-scale DNS global anycast network, after discounting the relevant deployment differences between the two networks. We find that regional anycast can effectively mitigate the pathology in global IP anycast where BGP routes a client’s traffic to a distant CDN site (e.g., a site in a different continent). However, DNS mapping inefficiencies, where DNS returns a sub-optimal regional IP anycast address that does not cover a client’s low-latency CDN sites, can harm regional anycast’s performance. Finally, using the Tangled testbed, we show what performance benefit regional IP anycast can achieve if we discount DNS mapping sub-optimality.

We also include a measurement work about the ever-increasing anycast flipping. We observe an increase in flipping over the past several years, reaching 4.4% of RIPE Atlas vantage points in 2023. We present evidence that the prevalence of anycast flipping is increasing, and for a small but not negligible portion of clients, the impact on web performance is significant.