Browsing by Author "Zhang, Yu"
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Item Chromatographic selectivity and hyper-crosslinked liquid chromatography stationary phases.(2010-01) Zhang, YuThe development of new stationary phases have always been of great interest in HPLC and has become increasingly important in recent years mainly driven by rapidly evolving industrial need as well as the quest for higher throughput HPLC analyses for better resolution and higher sensitivity. In this thesis, we developed a family of silica based RPLC stationary phases based on a novel hyper-crosslinked (HC) platform, prepared through a multi-layer, two-dimensional, orthogonal polymerization reaction. The resulting stationary phases showed better stability, higher efficiency and novel selectivities, which are the three essentials properties of the stationary phase that users are looking for and column developers strive to achieve. We first studied the synthesis and full characterization of a novel mixed-mode reversed-phase/weak cation exchange (RP/WCE) phase by introducing a small amount of carboxylate functionality into a hydrophobic hyper-crosslinked (HC) platform. The phase thus prepared shows a mixed-mode retention mechanism, allowing for both neutral organic compounds and charged bases to be separated simultaneously on the same phase under the same conditions. More importantly, the inherent weak cation exchange groups allow simple mobile phases to be used thereby avoiding the mass spectrometric ionization suppression problems concomitant to the use of non-volatile additives such as strong amine modifiers (e.g. triethylamine) to elute basic solutes from the strong cation exchange phases or ion pairing reagents (e.g. trifluoroacetic acid, ClO4-) to retain these solutes on conventional ODS phases. We next studied the development of a highly hydrophilic HC-OH phase prepared by hydrolyzing residual benzyl chloride groups on the hydrophobic platform. This phase is potentially useful as a candidate for use as the first dimension phase of comprehensive two-dimensional LC where column stability and low retentivity are greatly desired. We also developed a novel graphical method, the phase selectivity triangle plots, for visualizing the effect of surface chemistry (e.g. C18 vs. Phenyl vs. Fluoro) on stationary phase selectivities. The use of the new plots assists the selection of appropriate stationary phases for method development in both isocratic and gradient elution.Item Friend Group Diversity and Civic Engagement Among Adolescents(2022-04) Zhang, YuItem Resource Management for Sustainable Power Grids and Wireless Networks: Distributed and Robust Designs(2015-07) Zhang, YuOptimal management plays an indispensable role in judiciously allocating the surging demand of limited resources available to our modern society. Intelligent management schemes must be efficient, scalable, and even robust to the inherently uncertain and possibly adversarial nature. Leveraging state-of-the-art optimization and signal processing techniques, the present thesis addresses several fundamental issues and emerging challenges of cyber-physical systems, especially for the smart power grid and wireless networks. Robust energy management is first dealt with for a grid-connected microgrid featuring distributed energy sources. To address the intrinsic challenge of maintaining the supply-demand balance due to stochastic availability of renewable energy sources (RES), a novel power scheduling strategy is introduced to minimize the microgrid operational cost including the worst-case energy transaction cost. The resulting optimization problem is solved in a distributed fashion by each local controller via the dual decomposition approach. In addition, for an islanded microgrid or the long-term planning of the bulk power system, risk-limiting energy management using the loss-of-load probability is developed. Day-ahead stochastic market clearing with high-penetration wind energy is further pursued based on the DC optimal power flow model. Capitalizing on the conditional value-at-risk, the novel model is able to mitigate the potentially high risk of the recourse actions to account for wind forecast errors. To cope with possibly large-scale dispatchable loads, fast distributed solvers are developed with guaranteed convergence. This thesis also caters to distributed resource allocation in wireless networks. Robust transceiver design and energy scheduling are considered for multiple-input multiple-output cognitive radio networks, as well as smart-grid powered coordinated multipoint systems. Robust optimization problems are formulated to tackle the uncertainties from imperfect channel state information and the nondispatchable RES. Efficient distributed solvers are tailored to the resulting convex programs through the techniques of semi-definite relaxation, primal, and dual decomposition. Numerical results are reported to corroborate the merits of the novel framework, and assess performance of the proposed approaches.