Microchip capillary electrophoresis: improvements using detection geometry, on-line preconcentration and surface modification

2012 Summer.Includes bibliographical references.Capillary electrophoresis and related microfluidic technologies have been utilized with great success for a variety of bioanalytical applications. Microchip capillary electrophoresis (MCE) has the advantages of decreased analysis time, integrated sample processing, high portability, high throughput, minimal reagent consumption, and low analysis cost. This thesis will focus on the optimization of our previous microchip capillary electrophoresis coupled electrochemical detection (MCE-ECD) design for improved separation and detection performance using detection geometry, on-line preconcentration and surface modification. The first effort to improve detection sensitivity and limits of detection (LODs) of our previous MCE-ECD system is established by an implementation of a capillary expansion (bubble cell) at the detection zone. Bubble cell widths were varied from 1× to 10× the separation channel width (50 μm) to investigate the effects of electrode surface area on detection sensitivity, LOD, and separation efficiency. Improved detection sensitivity and decreased LODs were obtained with increased bubble cell width, and LODs of dopamine and catechol detected in a 5× bubble cell were 25 nM and 50 nM respectively. In addition, fluorescent imaging results demonstrate ~8% to ~12% loss in separation efficiency in 4× and 5× bubble cell, respectively. Another effort for enhancing detection sensitivity and reducing LODs involves using field amplified sample injection and field amplified sample stacking. Stacking effects were shown for both methods using DC amperometric and pulsed amperometric detections. Decreased LODs of dopamine were achieved using both on-line sample preconcentration methods. The use of mixed surfactants to affect electroosmotic flow (EOF) and alter separation selectivity for electrophoretic separations in poly(dimethylsiloxane) (PDMS) is also presented in this thesis. First the effect of surfactant concentration on EOF was studied using the current monitoring method for a single anionic surfactant (sodium dodecyl sulfate, SDS), a single zwitterionic surfactant (N-tetradecylammonium-N,N-dimethyl-3-ammonio-1-propane sulfonate, TDAPS), and a mixed ionic/zwitterionic surfactant system (SDS/TDAPS). SDS increases the EOF as reported previously while TDAPS shows an initial increase in EOF followed by a reduction in EOF at higher concentrations. The addition of TDAPS to a solution containing SDS makes the EOF decrease in a concentration dependent manner. The mixed SDS/TDAPS surfactant system allows tuning of the EOF across a range of pH and concentration conditions. After establishing EOF behavior, the adsorption/desorption rates were measured and show a slower adsorption/desorption rate for TDAPS than SDS. Next, capacitively coupled contactless conductivity detection (C4D) is introduced for EOF measurements on PDMS microchips as an alternative to the current monitoring method to improve measurement reproducibility. EOF measurements as a function of the surfactant concentration were performed simultaneously using both methods for three nonionic surfactants, (polyoxyethylene (20) sorbitan monolaurate (Tween 20), polyoxyethylene octyl phenyl ether (Triton X-100), polyethylene glycol, (PEG 400)), mixed ionic/nonionic surfactant systems (SDS/Tween 20, SDS/Triton X-100, and SDS/PEG 400) and mixed zwitterionic/nonionic surfactant systems (TDAPS/Tween 20, TDAPS/Triton X-100, and TDAPS/PEG 400). EOF for the nonionic surfactants decreases with increasing surfactant concentration. The addition of SDS or TDAPS to a nonionic surfactant increases EOF relative to the pure nonionic surfactant. Next, separation and electrochemical detection of two groups of model analytes were explored using mixed surfactant systems. Similar analyte resolution with greater peak heights was achieved with mixed surfactant systems relative to the single surfactant system. Finally, the utility of mixed surfactant systems to achieve improved separation chemistry of biologically relevant compounds in complex sample matrixes was demonstrated in two applications, which include the detection of catecholamine release from rat pheochromocytoma (PC12) cells by stimulation with 80 mM K+ and the detection of reduced glutathione (GSH) in red blood cells (RBCs) exposed to fly ash suspension as a model environmental oxidant

Analysis of marine container terminal gate congestion, truck waiting cost, and system optimization

As world container volume continues to grow and the introduction of 12,000 TEUs plus containerships into major trade routes, the port industry is under pressure to deal with the ever increasing freight volume. Gate congestion at marine container terminal is considered a major issue facing truckers who come to the terminal for container pickup and delivery. Harbor truckers operate in a very competitive environment; they are paid by trip, not by the hours they drive. Gate congestion is not only detrimental to their economic well-being, but also causes environmental pollution. This thesis applies a multi-server queuing model to analyze marine terminal gate congestion and quantify truck waiting cost. In addition, an optimization model is developed to minimize gate system cost. Extensive data collection includes field observations and online camera observation and terminal day-to-day operation records. Comprehensive data analysis provides a solid foundation to support the development of the optimization model. The queuing analysis indicates that there is a substantial truck waiting cost incurred during peak season. Three optimization alternatives are explored. The results prove that optimization by appointment is the most effective way to reduce gate congestion and improve system efficiency. Lastly, it is the recommendation to use the combination of optimization by appointment and productivity improvement to mitigate terminal gate congestion and accommodate the ever growing container volume

R&D and Technology Transfer: Firm-Level Evidence from Chinese Industry

The capacity of developing economies to narrow the gap in living standards with the OECD nations depends critically on their ability to imitate and innovate new technologies. Toward this end, developing economies have access to three avenues of technological advance: technology transfer, domestic R&D, and foreign direct investment. This paper examines the contributions of each of these avenues, as well as their interactions, to productivity and knowledge production within Chinese industry. Based on a large data set for China’s large and medium-size enterprises, the estimation results show that technology transfer – whether domestic or foreign – affects productivity only through its interactions with in-house R&D. Foreign direct investment does not appear to facilitate the adoption of market-mediated foreign technology transfer. Firms wishing to produce patentable knowledge do not benefit from technology transfer; patentable knowledge is created exclusively through in-house R&D operations.http://deepblue.lib.umich.edu/bitstream/2027.42/39968/3/wp582.pd

A Unified Framework for Causal Inference with Multiple Imputation Using Martingale

Multiple imputation is widely used to handle confounders missing at random in causal inference. Although Rubin's combining rule is simple, it is not clear whether or not the standard multiple imputation inference is consistent when coupled with the commonly-used average causal effect (ACE) estimators. This article establishes a unified martingale representation for the average causal effect (ACE) estimators after multiple imputation. This representation invokes the wild bootstrap inference to provide consistent variance estimation. Our framework applies to asymptotically normal ACE estimators, including the regression imputation, weighting, and matching estimators. We extend to the scenarios when both outcome and confounders are subject to missingness and when the data are missing not at random