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- All Subjects: Separation
- Genre: Academic theses
- Creators: Levitus, Marcia
- Creators: McIntyre, Sean
- Creators: Montgomery, Douglas C.
- Member of: ASU Electronic Theses and Dissertations
Description
Rapid and reliable separation and analysis of proteins require powerful analytical methods. The analysis of proteins becomes especially challenging when only small sample volumes are available, concomitantly with low concentrations of proteins. Time critical situations pose additional challenges. Due to these challenges, conventional macro-scale separation techniques reach their limitations. While microfluidic devices require only pL-nL sample volumes, they offer several advantages such as speed, efficiency, and high throughput. This work elucidates the capability to manipulate proteins in a rapid and reliable manner with a novel migration technique, namely dielectrophoresis (DEP). Since protein analysis can often be achieved through a combination of orthogonal techniques, adding DEP as a gradient technique to the portfolio of protein manipulation methods can extend and improve combinatorial approaches. To this aim, microfluidic devices tailored with integrated insulating obstacles were fabricated to create inhomogeneous electric fields evoking insulator-based DEP (iDEP). A main focus of this work was the development of pre-concentration devices where topological micropost arrays are fabricated using standard photo- and soft lithographic techniques. With these devices, positive DEP-driven streaming of proteins was demonstrated for the first time using immunoglobulin G (IgG) and bovine serum albumin. Experimentally observed iDEP concentrations of both proteins were in excellent agreement with positive DEP concentration profiles obtained by numerical simulations. Moreover, the micropost iDEP devices were improved by introducing nano-constrictions with focused ion beam milling with which numerical simulations suggested enhancement of the DEP effect, leading to a 12-fold increase in concentration of IgG. Additionally, concentration of β-galactosidase was observed, which seems to occur due to an interplay of negative DEP, electroosmosis, electrokinesis, diffusion, and ion concentration polarization. A detailed study was performed to investigate factors influencing protein DEP under DC conditions, including electroosmosis, electrophoresis, and Joule heating. Specifically, temperature rise within the iDEP device due to Joule heating was measured experimentally with spatial and temporal resolution by employing the thermosensitive dye Rhodamine B. Unlike DNA and cells, protein DEP behavior is not well understood to date. Therefore, this detailed study of protein DEP provides novel information to eventually optimize this protein migration method for pre-concentration, separation, and fractionation.
ContributorsNakano, Asuka (Author) / Ros, Alexandra (Thesis advisor) / Hayes, Mark (Committee member) / Levitus, Marcia (Committee member) / Arizona State University (Publisher)
Created2014
Description
The large-scale anthropogenic emission of carbon dioxide into the atmosphere leads to many unintended consequences, from rising sea levels to ocean acidification. While a clean energy infrastructure is growing, mid-term strategies that are compatible with the current infrastructure should be developed. Carbon capture and storage in fossil-fuel power plants is one way to avoid our current gigaton-scale emission of carbon dioxide into the atmosphere. However, for this to be possible, separation techniques are necessary to remove the nitrogen from air before combustion or from the flue gas after combustion. Metal-organic frameworks (MOFs) are a relatively new class of porous material that show great promise for adsorptive separation processes. Here, potential mechanisms of O2/N2 separation and CO2/N2 separation are explored.
First, a logical categorization of potential adsorptive separation mechanisms in MOFs is outlined by comparing existing data with previously studied materials. Size-selective adsorptive separation is investigated for both gas systems using molecular simulations. A correlation between size-selective equilibrium adsorptive separation capabilities and pore diameter is established in materials with complex pore distributions. A method of generating mobile extra-framework cations which drastically increase adsorptive selectivity toward nitrogen over oxygen via electrostatic interactions is explored through experiments and simulations. Finally, deposition of redox-active ferrocene molecules into systematically generated defects is shown to be an effective method of increasing selectivity towards oxygen.
First, a logical categorization of potential adsorptive separation mechanisms in MOFs is outlined by comparing existing data with previously studied materials. Size-selective adsorptive separation is investigated for both gas systems using molecular simulations. A correlation between size-selective equilibrium adsorptive separation capabilities and pore diameter is established in materials with complex pore distributions. A method of generating mobile extra-framework cations which drastically increase adsorptive selectivity toward nitrogen over oxygen via electrostatic interactions is explored through experiments and simulations. Finally, deposition of redox-active ferrocene molecules into systematically generated defects is shown to be an effective method of increasing selectivity towards oxygen.
ContributorsMcIntyre, Sean (Author) / Mu, Bin (Thesis advisor) / Green, Matthew (Committee member) / Lind, Marylaura (Committee member) / Arizona State University (Publisher)
Created2019
Description
Optimal design theory provides a general framework for the construction of experimental designs for categorical responses. For a binary response, where the possible result is one of two outcomes, the logistic regression model is widely used to relate a set of experimental factors with the probability of a positive (or negative) outcome. This research investigates and proposes alternative designs to alleviate the problem of separation in small-sample D-optimal designs for the logistic regression model. Separation causes the non-existence of maximum likelihood parameter estimates and presents a serious problem for model fitting purposes.
First, it is shown that exact, multi-factor D-optimal designs for the logistic regression model can be susceptible to separation. Several logistic regression models are specified, and exact D-optimal designs of fixed sizes are constructed for each model. Sets of simulated response data are generated to estimate the probability of separation in each design. This study proves through simulation that small-sample D-optimal designs are prone to separation and that separation risk is dependent on the specified model. Additionally, it is demonstrated that exact designs of equal size constructed for the same models may have significantly different chances of encountering separation.
The second portion of this research establishes an effective strategy for augmentation, where additional design runs are judiciously added to eliminate separation that has occurred in an initial design. A simulation study is used to demonstrate that augmenting runs in regions of maximum prediction variance (MPV), where the predicted probability of either response category is 50%, most reliably eliminates separation. However, it is also shown that MPV augmentation tends to yield augmented designs with lower D-efficiencies.
The final portion of this research proposes a novel compound optimality criterion, DMP, that is used to construct locally optimal and robust compromise designs. A two-phase coordinate exchange algorithm is implemented to construct exact locally DMP-optimal designs. To address design dependence issues, a maximin strategy is proposed for designating a robust DMP-optimal design. A case study demonstrates that the maximin DMP-optimal design maintains comparable D-efficiencies to a corresponding Bayesian D-optimal design while offering significantly improved separation performance.
First, it is shown that exact, multi-factor D-optimal designs for the logistic regression model can be susceptible to separation. Several logistic regression models are specified, and exact D-optimal designs of fixed sizes are constructed for each model. Sets of simulated response data are generated to estimate the probability of separation in each design. This study proves through simulation that small-sample D-optimal designs are prone to separation and that separation risk is dependent on the specified model. Additionally, it is demonstrated that exact designs of equal size constructed for the same models may have significantly different chances of encountering separation.
The second portion of this research establishes an effective strategy for augmentation, where additional design runs are judiciously added to eliminate separation that has occurred in an initial design. A simulation study is used to demonstrate that augmenting runs in regions of maximum prediction variance (MPV), where the predicted probability of either response category is 50%, most reliably eliminates separation. However, it is also shown that MPV augmentation tends to yield augmented designs with lower D-efficiencies.
The final portion of this research proposes a novel compound optimality criterion, DMP, that is used to construct locally optimal and robust compromise designs. A two-phase coordinate exchange algorithm is implemented to construct exact locally DMP-optimal designs. To address design dependence issues, a maximin strategy is proposed for designating a robust DMP-optimal design. A case study demonstrates that the maximin DMP-optimal design maintains comparable D-efficiencies to a corresponding Bayesian D-optimal design while offering significantly improved separation performance.
ContributorsPark, Anson Robert (Author) / Montgomery, Douglas C. (Thesis advisor) / Mancenido, Michelle V (Thesis advisor) / Escobedo, Adolfo R. (Committee member) / Pan, Rong (Committee member) / Arizona State University (Publisher)
Created2019