VTechWorks Repository :: Browsing by Author "Achenie, Luke E. K."

Browsing by Author "Achenie, Luke E. K."

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Accelerating Catalyst Discovery via Ab Initio Machine Learning
Li, Zheng (Virginia Tech, 2019-12-03)
In recent decades, machine learning techniques have received an explosion of interest in the domain of high-throughput materials discovery, which is largely attributed to the fastgrowing development of quantum-chemical methods and learning algorithms. Nevertheless, machine learning for catalysis is still at its initial stage due to our insufficient knowledge of the structure-property relationships. In this regard, we demonstrate a holistic machine-learning framework as surrogate models for the expensive density functional theory to facilitate the discovery of high-performance catalysts. The framework, which integrates the descriptor-based kinetic analysis, material fingerprinting and machine learning algorithms, can rapidly explore a broad range of materials space with enormous compositional and configurational degrees of freedom prior to the expensive quantum-chemical calculations and/or experimental testing. Importantly, advanced machine learning approaches (e.g., global sensitivity analysis, principal component analysis, and exploratory analysis) can be utilized to shed light on the underlying physical factors governing the catalytic activity on a diverse type of catalytic materials with different applications. Chapter 1 introduces some basic concepts and knowledge relating to the computational catalyst design. Chapter 2 and Chapter 3 demonstrate the methodology to construct the machine-learning models for bimetallic catalysts. In Chapter 4, the multi-functionality of the machine-learning models is illustrated to understand the metalloporphyrin's underlying structure-property relationships. In Chapter 5, an uncertainty-guided machine learning strategy is introduced to tackle the challenge of data deficiency for perovskite electrode materials design in the electrochemical water splitting cell.
Advancing Microbial Desalination Cell towards Practical Applications
Ping, Qingyun (Virginia Tech, 2016-11-03)
Conventional desalination plant, municipal water supply and wastewater treatment system are among the most electricity-intensive facilities. Microbial Desalination Cell (MDC) has emerged as a promising technique to capture the chemical energy stored in wastewater directly for desalination, which has the potential to solve the high energy consumption issue in desalination industry as well as wastewater treatment system. The MDC is composed of two critical components, the electrodes (anode and cathode), and the ion-exchange membranes separating the two electrodes which drive anions migrate towards the anode, and cations migrate towards the cathode. The multiple components allow us to manipulate the configuration to achieve most efficient desalination performance. By coupling with Donnan Dialysis or Microbial Fuel Cell, the device can effectively achieve boron removal which has been a critical issue in desalination plants. The uncertainty of water quality of the final desalinated water caused by contaminant back diffusion from the wastewater side can be theoretically explained by two mechanisms, Donnan exchange and molecule transport which are controlled by bioelectricity and concentration gradient. Scaling and fouling is also a factor needs to be taken into consideration when operating the MDC system in real world. With mathematical modeling, we can provide insight to bridge the gap between lab-scale experiments and industrial applications. This study is expected to provide guidance to enhance the efficiency as well as the reliability and controllability of MDC for desalination.
Assessing an Orientation Model and Stress Tensor for Semi-Flexible Glass Fibers in Polypropylene Using a Sliding Plate Rheometer: for the Use of Simulating Processes
Ortman, Kevin Charles (Virginia Tech, 2011-08-05)
Great interest exists in adding long fibers into polymeric fluids due to the increase in properties associated with the composite, as compared to the neat resin. These properties, however, are dependent on the fiber orientations generated during processing, such as injection molding. In an effort to optimize industrial processing, optimize mold design, and maximize desired properties of the final part, it is highly desirable to predict long fiber orientation as a function of processing conditions. The purpose of this research is to use rheology as a fundamental means of understanding the transient orientation behavior of concentrated long glass (> 1mm) fiber suspensions. Specifically, this research explores the method of using rheology as a means of obtaining stress tensor and orientation model parameters needed to accurately predict the transient fiber orientation of long glass fiber reinforced polypropylene, in a well-defined simple shear flow, with the hopes of extending the knowledge gained from these fundamental experiments for the use of simulating processing flows, such as injection molding. Two fiber orientation models were investigated to predict the transient orientation of the long glass fiber systems explored. One model, the Folgar-Tucker model, has been particularly useful for predicting fiber orientation in short glass fiber systems and was used in this paper to assess its performance with long glass fibers. A second orientation model, one that accounts for the semi-flexibility of fibers, was extended to describe non-dilute suspension and coupled with an augmented stress tensor that accounts for fiber bending. Stress tensor and orientation model parameters were determined (in all cases) by best fitting these coupled equations to measured stress data obtained using a sliding plate rheometer. Results showed the semi-flexible orientation model and stress tensor combination, overall, provided improved rheological results as compared to the Folgar-Tucker model when coupled with the stress tensor of Lipscomb (1988). Furthermore, it was found that both stress tensors required empirical modification to accurately fit the measured data. Both orientation models provided encouraging results when predicting the transient fiber orientation in a sliding plate rheometer, for all initial fiber orientations explored. Additionally, both orientation models provided encouraging results when the model parameters, determined from the rheological study, were used for the purpose of predicting fiber orientation in an injection molded center-gated disk.
Calcium/Phosphate Regulation: A Control Engineering Approach
Christie, Christopher Robert (Virginia Tech, 2014-01-10)
Calcium (Ca) homeostasis is the maintenance of a stable plasma Ca concentration in the human body in the presence of Ca variability in the physiological environment (e.g. by ingestion and/or excretion). For normal physiological function, the total plasma Ca concentration must be maintained within a very narrow range (2.2-2.4mM). Meeting such stringent requirements is the task of a regulatory system that employs parathyroid hormone (PTH) and calcitriol (CTL) to regulate Ca flux between the plasma and the kidneys, intestines and bones. On the other hand, plasma phosphate control is less tightly, but simultaneously, regulated via the same hormonal actions. Chronic imbalances in plasma Ca levels are associated with disorders of the regulatory organs, which cause abnormal hormonal secretion and activity. These changes in hormonal activity may lead to long-term problems, such as, osteoporosis (increased loss of bone mineral density), which arises from primary hyperparathyroidism (PHPT) – hyper secretion of PTH. Existing in silico models of Ca homeostasis in humans are often cast in the form of a single monolithic system of differential equations and are not easily amenable to the sort of tractable quantitative analysis from which one can acquire useful fundamental insight. In this research, the regulatory systems of plasma Ca and plasma phosphate are represented as an engineering control system where the physiological sub-processes are mapped onto corresponding block components (sensor, controller, actuator and process) and underlying mechanisms are represented by differential equations. Following validation of the overall model, Ca-related pathologies are successfully simulated through induced defects in the control system components. A systematic approach is used to differentiate PHPT from other diseases with similar pathophysiologies based on the unique hormone/ion responses to short-term Ca disturbance in each pathology model. Additionally, based on the changes in intrinsic parameters associated with PTG behavior, the extent of PHPT progression can be predicted and the enlarged gland size estimated a priori. Finally, process systems engineering methods are used to explore therapeutic intervention in two Ca-related pathologies: Primary (PHPT) and Secondary (SHPT) Hyperparathyroidism. Through parametric sensitivity analysis and parameter space exploration, the calcium-sensing receptor (sensor) is identified as a target site in both diseases and the extent of potential improvement is determined across the spectrum of severity of PHPT. The findings are validated against existing drug therapy, leading to a method of predicting drug dosage for a given stage of PHPT. Model Predictive Control is used in drug therapy in SHPT to customize the drug dosage for individual patients given the desired PTH outcome, and drug administration constraints.
Catalytic Hydrogenation and Hydrodesulfurization of Model Compounds
Zhao, Haiyan (Virginia Tech, 2009-03-19)
This dissertation describes two related studies on hydrogenation and hydrodesulfurization of heterocyclic S-containing compounds. Alkyl substituted thiophenes are promising candidates for hydrogen carriers as the dehydrogenation reactions are known to occur under mild conditions. Four types of catalysts including supported noble metals, bimetallic noble metals, transition metal phosphides and transition metal sulfides have been investigated for 2-methylthiophene (2MT) hydrogenation and ring opening. The major products were tetrahydro-2-methylthiophene (TH2MT), pentenes and pentane, with very little C5-thiols observed. The selectivity towards the desired product TH2MT follows the order: noble metals > bimetallics > phosphides > sulfides. The best hydrogenation catalyst was 2% Pt/Al2O3 which exhibited relatively high reactivity and selectivity towards TH2MT at moderate temperatures. Temperature-programmed desorption (TPD) of hydrogen indicated that the H2 desorption amount was inversely related to the rate of TH2MT formation. Temperature programmed reaction (TPR) experiments revealed that pentanethiol became the major product, especially with HDS catalysts like CoMoS/Al2O3 and WP/SiO2, which indicates that poisoned or modified conventional HDS catalysts would be good candidates for further 2MT hydrogenation studies. The role of tetrahedral Ni(1) sites and square pyramidal Ni(2) sites in Ni2P hydrotreating catalysts was studied by substitution of Ni with Fe. The Fe component was deemed as a good probe because Ni2P and Fe2P adopt the same hexagonal crystal structure, yet Fe2P is completely inactive for hydrodesulfurization (HDS). For this purpose a series of NiFeP/SiO2 catalysts were prepared with different Ni:Fe molar ratios (1:0, 3:1, 1:1, 1:3, and 0:1) and investigated in the HDS of 4,6-dimethyldibenzothiophene at 300 and 340 oC. The uniformity of the NiFe series was demonstrated by x-ray diffraction analysis and by Fourier transform infrared (FTIR) spectroscopy of adsorbed CO. The position of substitution of Fe was determined by extended X-ray absorption fine structure (EXAFS) analysis. It was found that at 300 oC the HDS activity of the catalysts decreased with increasing Fe content and that this could be explained by the substitution of Fe at the more active Ni(2) sites. As temperature was raised to 340 oC, the activity of the Fe-containing samples increased, although not to the level of Ni2P, and this could be understood from a reconstruction of the NiFe phase to expose more Ni(2) sites. This was likely driven by the formation of surface Ni-S bonds, which could be observed by EXAFS in spent samples.
Computational models in plant-pathogen interactions: the case of Phytophthora infestans
Pinzón, Andrés; Barreto, Emiliano; Bernal, Adriana; Achenie, Luke E. K.; González Barrios, Andrés Fernando; Isea, Raúl; Restrepo, Silvia (2009-11-12)
Background Phytophthora infestans is a devastating oomycete pathogen of potato production worldwide. This review explores the use of computational models for studying the molecular interactions between P. infestans and one of its hosts, Solanum tuberosum. Modeling and conclusion Deterministic logistics models have been widely used to study pathogenicity mechanisms since the early 1950s, and have focused on processes at higher biological resolution levels. In recent years, owing to the availability of high throughput biological data and computational resources, interest in stochastic modeling of plant-pathogen interactions has grown. Stochastic models better reflect the behavior of biological systems. Most modern approaches to plant pathology modeling require molecular kinetics information. Unfortunately, this information is not available for many plant pathogens, including P. infestans. Boolean formalism has compensated for the lack of kinetics; this is especially the case where comparative genomics, protein-protein interactions and differential gene expression are the most common data resources.
Continuous flash extraction of alcohols from fermentation broth
Teye, Frederick David (Virginia Tech, 2009-02-04)
A new method of in situ extraction of alcohols from fermentation broth was investigated. The extraction method exploited the latent advantages of the non-equilibrium phase interaction of the fluid system in the flash tank to effectively recover the alcohol. Carbon dioxide gas ranging from 4.2L/min to 12.6L/min was used to continuously strip 2 and 12% (v/v) ethanol solution in a fermentor with a recycle. Ethanol and water in the stripped gas was recovered by compressing and then flashing into a flash tank that was maintained at 5 to 70bar and 5 to 55oC where two immiscible phases comprising CO2-rich phase (top layer) and H2O-rich phase (bottom layer) were formed. The H2O-rich bottom layer was collected as the Bottoms. The CO2-rich phase was continuously throttled producing a condensate (Tops) as a result of the Joule-Thompson cooling effect. The total ethanol recovered from the extraction scheme was 46.0 to 80% for the fermentor containing 2% (v/v) ethanol and 57 to 89% for the fermentor containing 12% (v/v) ethanol. The concentration of ethanol in the Bottoms ranged from 8.0 to 14.9 %(v/v) for the extraction from the 2 %(v/v) ethanol solution and 40.0 to 53.8 %(v/v) for the 12% (v/v) fermentor ethanol extraction. The Bottoms concentration showed a fourfold increase compared to the feed. The ethanol concentration of the Tops were much higher with the highest at approx. 90% (v/v) ethanol, however the yields were extremely low. Compression work required ranged from 6.4 to 20.1 MJ/kg ethanol recovered from the gas stream in the case of 12% (v/v) ethanol in fermentor. The energy requirement for the 2% (v/v) extraction was 84MJ/kg recovered ethanol. The measured Joule-Thompson cooling effect for the extraction scheme was in the range of 10 to 20% the work of compressing the gas. The lowest measured throttle valve temperature was -47oC at the flash tank conditions of 70bar and 25oC. Optimization of the extraction scheme showed that increasing the temperature of the flash tank reduced the amount of ethanol recovered. Increasing the pressure of the flash tank increased the total ethanol recovered but beyond 45bar it appeared to reduce the yield. The 12.6L/min carbon dioxide flow rate favored the high pressure(70bar) extraction whiles 4.2L/min appeared to favor the low pressure(40bar) extraction. The studies showed that the extraction method could potentially be used to recover ethanol and other fermentation products.
Corticosteroid-Encapsulated Nanoparticles in Thermoreversible Gels for the Amelioration of Choroidal Neovascularization in Age-Related Macular Degeneration
Hirani, Anjali A. (Virginia Tech, 2015-04-30)
Age-related macular degeneration (AMD) is one of the leading causes of blindness in adults over the age of 60. Currently, at least 11 million patients in the United States have some form of macular degeneration and this number is projected to grow as the population ages. The more severe form of the disease – neovascular (wet) AMD, is characterized by intraocular neovascularization, inflammation, and retinal damage; however, the disease progression can be deterred through intraocular injections of anti-angiogenic agents. The complications and burden that arise from repetitive injections as well as the difficulty posed by targeting the posterior segment of the eye make this an interesting territory for the development of novel drug delivery systems. New methods for drug delivery are being investigated exploring the use of nanoparticles and other polymeric materials. The goal of this project is to study the potential use of poly(lactide-co-glycolic acid)-polyethylene glycol (PLGA-PEG) nanoparticles in thermoreversible gels as localized sustained intraocular drug delivery. We prepared stable and reproducible corticosteroid-encapsulated nanoparticles in thermoreversible gels to inhibit vascular endothelial growth factor (VEGF) overexpression characteristic of neovascular AMD. We characterized the drug delivery system by obtaining size, shape, and drug encapsulation data. We also demonstrated that the polymer could be injected into the vitreous as a solution and transition to a gel phase based on the temperature difference between regular indoor environment and the vitreous body. The drug delivery system was tested on human retinal pigment epithelial cells (ARPE-19), for cytotoxicity, uptake and VEGF expression. We also examined the drug delivery system's ability to mitigate the disease progression in a mouse model of choroidal neovascularization (CNV). The effect on blood vessel area was shown and the changes in the mRNA expression of angiogenesis mediators were analyzed by real-time reverse transcription polymerase chain reaction (RT-PCR). These results indicate that the proposed drug delivery systems has the promise to be developed for retinal diseases, involving CNV, including neovascular AMD. Further studies are warranted in developing this promising intraocular drug delivery system for wet AMD and similar ophthalmic diseases.
Design and Optimization of Post-Combustion CO2 Capture
Higgins, Stuart James (Virginia Tech, 2016-05-17)
This dissertation describes the design and optimization of a CO2-capture unit using aqueous amines to remove of carbon dioxide from the flue gas of a coal-fired power plant. In particular we construct a monolithic model of a carbon capture unit and conduct a rigorous optimization to find the lowest solvent regeneration energy yet reported. Carbon capture is primarily motivated by environmental concerns. The goal of our work is to help make carbon capture and storage (CCS) a more efficient for the sort of universal deployment called for by the Intergovernmental Panel on Climate Change (IPCC) to stabilize anthropomorphic contributions to climate change, though there are commercial applications such as enhanced oil recovery (EOR). We employ the latest simulation tools from Aspen Tech to rigorously model, design, and optimize acid gas systems. We extend this modeling approach to leverage Aspen Plus in the .NET framework through Microsoft's Component Object Model (COM). Our work successfully increases the efficiency of acid gas capture. We report a result optimally implementing multiple energy-saving schemes to reach a thermal regeneration energy of 1.67 GJ/tonne. By contrast, the IPCC had reported that leading technologies range from 2.7 to 3.3 GJ/tonne in 2005. Our work has received significant endorsement for industrial implementation by the senior management from the world's second largest chemical corporation, Sinopec, as being the most efficient technology known today.
Development of Microfluidic Platforms for Electric Field-Driven Drug Delivery and Cell Migration
Moarefian, Maryam (Virginia Tech, 2020-06-02)
Recent technologies in micro-devices for investigation of functional biology in a controlled microenvironment are continually growing and evolving. In particular, electric-field mediated microfluidic platforms are evolving technologies that have significant applications in drug delivery and cell migration investigations. Although drug delivery has had several successes, in some areas, it continues to be a challenge; in recent years, the positive impact of electric fields is being explored. The primary objectives of the dissertation are to design, fabricate, and employ two novel microfluidic platforms for drug delivery and cell migration in the presence of electric fields. Description of iontophoretic carboplatin delivery into the MDA-MB-231 triple-negative breast cancer cells and investigation of neutrophil electro taxis are two main aims of the dissertation. Transdermal drug delivery systems such as iontophoresis are useful tools for delivering chemotherapeutics for tumor treatment not only because of their non-invasiveness but also due to their lower systematic toxicity compared to other drug delivery systems. While iontophoresis animal models are commonly being used for the development of new cancer therapies, there are some obstacles for precise control of the tumor microenvironment's chemoresistance and scaffold in the animal models. We employed experimental and computational approaches, the iontophoresis-on-chip and the fraction of tumor killed mathematical model, for predicting the outcome of iontophoresis treatment in a controlled microenvironment. Also, precise control over the cell electromigration is a challenging investigation which we will address in the second aim of the dissertation. Here, we developed a microfluidic platform to study the consequences of DC electric fields on neutrophil electromigration (electrotaxis), which has an application of directing neutrophils away from healthy tissue by suppressing the migration of neutrophils toward pro-inflammatory chemoattractant.
Development of Transferable Coarse-Grained Models of Amino Acids
Conway, Olivia Kristine (Virginia Tech, 2019-10-01)
There are twenty standard amino acids that are the structural units of biomolecules and biomaterials such as proteins and peptide amphiphiles (PAs). The focus of this study was to develop accurate transferable coarse-grained (CG) models of those amino acids. In CG models, several atoms are represented together as a single pseudo-atom or "bead," which can allow the modeling of processes like self-assembly of biomolecules and biomaterials through reduction of degrees of freedom and corresponding increased computational speed. A 2:1 to 4:1 mapping scheme, in which a CG bead is comprised of two to four heavy atoms, respectively, and associated hydrogens, has been employed to represent functional groups in the amino acids. The amino acid backbone atoms are modeled as two beads while the side chains are modeled with one to three beads, and each terminus is modeled as one bead. The bonded parameters for the CG models were obtained from bond, angle, and dihedral distributions from all-atom molecular dynamics (MD) simulations of dipeptides. Non-bonded parameters were optimized using the particle swarm optimization (PSO) method to reproduce experimental properties (heat of vaporization, surface tension, and density) of analogues of the side chains, termini, and backbone groups of the amino acids. These CG models were used to study the self-assembly pathways and mechanisms of the PA c16-AHL3K3-CO2H in the presence of explicit CG water.
Diffusion-based Microfluidic PCR for "One-pot" Analysis of Cells
Ma, Sai; Loufakis, Despina N.; Cao, Zhenning; Chang, Yiwen; Achenie, Luke E. K.; Lu, Chang (The Royal Society of Chemistry, 2014-05-28)
Genetic analysis starting with cell samples often requires multi-step processing including cell lysis, DNA isolation/purification, and polymerase chain reaction (PCR) based assays. When conducted on a microfluidic platform, the compatibility among various steps often demands a complicated procedure and a complex device structure. Here we present a microfluidic device that permits a “one-pot” strategy for multi-step PCR analysis starting from cells. Taking advantage of the diffusivity difference, we replace the smaller molecules in the reaction chamber by diffusion while retaining DNA molecules inside. This simple scheme effectively removes reagents from the previous step to avoid interference and thus permits multi-step processing in the same reaction chamber. Our approach shows high efficiency for PCR and potential for a wide range of genetic analysis including assays based on single cells.
Effects of Febuxostat on Autistic Behaviors and Computational Investigations of Diffusion and Pharmacokinetics
Simmons, Jamelle Marquis (Virginia Tech, 2019-02-06)
Autism spectrum disorder (ASD) is a lifelong disability that has seen a rise in prevalence from 1 in 150 children to 1 in 59 between 2000 and 2014. Patients show behavioral abnormalities in the areas of social interaction, communication, and restrictive and repetitive behaviors. As of now, the exact cause of ASD is unknown and literature points to multiple causes. The work contained within this dissertation explored the reduction of oxidative stress in brain tissue induced by xanthine oxidase (XO). Febuxostat is a new FDA approved XO-inhibitor that has been shown to be more selective and potent than allopurinol in patients with gout. The first study developed a computational model to calculate an effective diffusion constant (Deff) of lipophilic compounds, such as febuxostat, that can cross endothelial cells of the blood-brain barrier (BBB) by the transcellular pathway. In the second study, male juvenile autistic (BTBR) mice were treated with febuxostat for seven days followed by behavioral testing and quantification of oxidative stress in brain tissue compared to controls. Results of the first study showed that the lipophilic tracer chosen, as a substitute for febuxostat, could be modeled under the assumption of passive diffusion while experimental controls did not fit this model. The second study revealed no significant differences between BTBR mice that received febuxostat or the drug vehicle in both behavioral testing and quantification of oxidative stress in brain tissue. In the final study, of the four models proposed, one model was selected as the most plausible that considered transport into the CNS. As there is currently no literature surrounding tissue and organ ADME for febuxostat the final proposed model would need to be updated as new information becomes available.
Enabling Dynamic Spectrum Access in 4G Networks and Beyond
Deaton, Juan Diego (Virginia Tech, 2012-05-30)
As early as 2014, mobile network operators' spectral capacity will be overwhelmed by the demand brought on by new devices and applications. To augment capacity and meet this demand, operators may choose to deploy a Dynamic Spectrum Access (DSA) overlay. Spectrum regulation is following suit, with regulators attempting to incorporate spectrum sharing through the design of spectrum access rules that support DSA. This dissertation explores the idea of DSA applied to Long Term Evolution Advanced (LTE+) networks. This idea is explored under functional, architectural, and spectrum policy aspects. Under the functional and architectural aspects of this topic, the signaling and functionality required by such an overlay have not yet been fully considered in the architecture of an LTE+. This dissertation presents a Spectrum Accountability framework to be integrated into LTE+ MacroNet and HetNet architectures, defining specific element functionality, protocol interfaces, and signaling flow diagrams required to enforce the rights and responsibilities of primary and secondary users. We also identify and propose three DSA management frameworks for LTE+ HetNets: Spectrum Accountability Client Only, Cell Spectrum Management, and Domain Spectrum Management. Our Spectrum Accountability framework may serve as a guide in the development of future LTE+ network standards that account for DSA. We also quantify, through simulation and integer programs, the benefits of using DSA channels to augment capacity under a scenario in which LTE+ network can opportunistically use TV and GSM spectrum. In our first experiment, we a consider a scenario where three different operators share the same cell site with LTE+ equipment and a Dynamic Spectrum Access (DSA) band to augment spectral capacity. Our experiments show that throughput can increase by as much as 40%. We develop integer programs to model the assignment of spectrum channels to both a MacroNet and HetNet. In our selected scenario, we observe TV white spectrum provides the largest gain in performance for both Nets: 27% for MacroNet and 9% increase for the HetNet over our measured ranges. Although the gains in using opportunistic use of GSM is more modest, 10% and 2% for the Macro and HetNet, respectively, we believe that these gains will significantly increase as operators continue to migrate users to LTE+, thus freeing up portions of the bands currently used for GSM service. In our final analytical model, we create integer program sets to represent the different three DSA management frameworks for LTE+ HetNets and compare their results. Under the spectrum policy aspects, this dissertation develops a decision-theoretic framework for regulators to assess the impacts of different spectrum access rules on both primary and secondary operators. We analyze access rules based on sensing and exclusion areas, which in practice can be enforced through geolocation databases. Our results show that receiver-only sensing provides insufficient protection for primary and co-existing secondary users and overall low social welfare. On the other hand, combining sensing information of only the transmitter and receiver of a communication link provides dramatic increases in system performance. The performance of using these link end points is relatively close to that of using many cooperative sensing nodes associated to the same access point and large link exclusion areas. We hope these results will prove useful to regulators and network developers in un and developing rules for future DSA regulation.
Exploring Strategies to Break Adsorption-Energy Scaling Relations in Catalytic CO Oxidation
Wang, Jiamin (Virginia Tech, 2020-01-21)
An atomistic control of chemical bonds formation and cleavage holds the key to making molecular transformations more energy efficient and product selective. However, inherent scaling relations among binding strengths of adsorbates on various catalytic materials often give rise to volcano-shaped relationships between the catalytic activity and the affinity of critical intermediates to the surface. The optimal catalysts should bind the reactants 'just right', i.e., neither too strong nor too weak, which is the Sabatier's principle. It is extremely useful for searching promising catalysts, but also imposes serious constraints on design flexibility. Therefore, how to circumvent scaling constraints is crucial for advancing catalytic science. It has been shown that hot electrons can selectively activate the chemical bonds that are not responsive to phonon excitation, thus providing a rational approach beyond scaling limitation. Another emerging yet effective way to break the scaling constraint is single atom catalysis. Strong interactions of supported single atoms with supports dramatically affect the electronic structure of active sites, which reroutes mechanistic pathways of surface reactions. In my PhD research, we use CO oxidation reaction on metal-based active sites as a benchmark system to tailor mechanistic pathways through those two strategies 1) ultra-fast laser induced nonadiabatic surface chemistry and 2) oxide-supported single metal catalysis, with the aim to go beyond the Sabatier activity volcano in metal catalysis.
Fabrication of Ultrathin Palladium Composite Membranes by a New Technique and Their Application in the Ethanol Steam Reforming for H₂ Production
Yun, Samhun (Virginia Tech, 2011-03-21)
This thesis describes a new technique for the preparation of ultrathin Pd based membranes supported on a hollow-fiber α-alumina substrate for H₂ separation. The effectiveness of the membranes is demonstrated in the ethanol steam reforming (EtOH SR) reaction in a membrane reactor (MR) for H₂ production. The membrane preparation technique uses an electric-field to uniformly deposit Pd nanoparticle seeds on a substrate followed by deposition of Pd or Pd-Cu layers on the activated surface by electroless plating (ELP). The well distributed Pd nanoparticles allow for enhanced bonding between the selective layer and the substrate and the formation of gas tight and thermally stable Pd or Pd-Cu layers as thin as 1 µm, which is a record in the field. The best Pd membrane showed H₂ permeance as high as 5.0 × 10⁶ mol m²s⁻¹Pa⁻¹ and stable H²/N₂ selectivity of 9000 - 7000 at 733 K for 5 days. The Pd-Cu alloy membrane showed H₂ permeance of 2.5 × 10⁶ mol m⁻²s⁻¹Pa⁻¹ and H₂/N₂ selectivity of 970 at the same conditions. The reaction studies were carried out with a Co-Na/ZnO catalyst both in a packed bed reactor (PBR) and in a MR equipped with the Pd or Pd-Cu membrane to evaluate the benefits of employing membranes. For all studies, ethanol conversion and hydrogen product yields were significantly higher in the MRs compared to the PBR. Average ethanol conversion enhancement and hydrogen molar flow enhancement were measured to be 12 % and 11 % in the Pd MR and 22 % and 19 % in the Pd-Cu MR, respectively. These enhancements of the conversion and product yield can be attributed to the shift in reaction equilibria by continuous hydrogen removal by the Pd based membranes. The comparative low enhancement in the Pd MR was found to be the result of significant contamination of Pd layer by CO or carbon compounds deposition during the reaction. A one-dimensional modeling of the MR and the PBR was conducted using identical conditions and their performances were compared with the values obtained from the experimental study. The model was developed using a simplified power law and the predicted values matched experimental data with only minor deviations indicating that the model was capturing the essential physicochemical behavior of the system. Enhancements of ethanol conversion and hydrogen yield were observed to increase with rise in space velocity (SV), which could be explained by the increase in H₂ flux through the membranes with SV in the MRs.
Impact of the Mode of Extraction on the Lipidomic Profile of Oils Obtained from Selected Amazonian Fruits
Cardona Jaramillo, Juliana Erika Cristina; Carrillo Bautista, Marcela Piedad; Alvarez Solano, Oscar Alberto; Achenie, Luke E. K.; González Barrios, Andrés Fernando (MDPI, 2019-08-01)
Oils and fats are important raw materials in food products, animal feed, cosmetics, and pharmaceuticals among others. The market today is dominated by oils derive, d from African palm, soybean, oilseed and animal fats. Colombia’s Amazon region has endemic palms such as Euterpe precatoria (açai), Oenocarpus bataua (patawa), and Mauritia flexuosa (buriti) which grow in abundance and produce a large amount of ethereal extract. However, as these oils have never been used for any economic purpose, little is known about their chemical composition or their potential as natural ingredients for the cosmetics or food industries. In order to fill this gap, we decided to characterize the lipids present in the fruits of these palms. We began by extracting the oils using mechanical and solvent-based approaches. The oils were evaluated by quantifying the quality indices and their lipidomic profiles. The main components of these profiles were triglycerides, followed by diglycerides, fatty acids, acylcarnitine, ceramides, ergosterol, lysophosphatidylcholine, phosphatidyl ethanolamine, and sphingolipids. The results suggest that solvent extraction helped increase the diglyceride concentration in the three analyzed fruits. Unsaturated lipids were predominant in all three fruits and triolein was the most abundant compound. Characterization of the oils provides important insights into the way they might behave as potential ingredients of a range of products. The sustainable use of these oils may have considerable economic potential.
Improved Prediction of Glass Fiber Orientation in Basic Injection Molding Geometries
Meyer, Kevin Joseph (Virginia Tech, 2013-12-18)
This work is concerned with the prediction of short (SGF) and long glass fiber (LGF) orientation in a center-gated disk and end-gated plaque injection molding test geometry using a simulation method that has not been attempted previously. Previous work has used assumptions to simplify the fiber orientation geometry (assuming a thin cavity) or flow field (neglecting fountain flow and entry regions). LGF orientation is predicted in a center-gated disk injection molding geometry including the advancing front and simulating the sprue and gate region (SGM method) so that no assumption about fiber orientation at the mold entrance has to be made. Using a semi-flexible fiber model and orientation parameters obtained through rheology, increased agreement was found between predicted and experimentally obtained values of orientation using the SGM method and a semi-flexible fiber model than was found using a Hele-Shaw approximation. The SGM method was applied to the end-gated plaque to predict SGF orientation both along and away from the centerline using an objective (reduced strain closure model) and non-objective (strain reduction factor model) orientation model. The predicted values of the strain reduction factor model showed reasonable agreement with experimentally obtained values of orientation throughout the three-dimensional cavity when using orientation parameters fit to experimental orientation data. Furthermore it was found that the objective model predicted results very similar to the non-objective model suggesting that objectivity may not play a role in predicting orientation in more complex geometries such as an end-gated plaque. Finally, the SGM method was applied to the end-gated plaque geometry to predict LGF orientation using a rigid and semi-flexible fiber model. It was found that the SGM method and the semi-flexible fiber model provides orientation predictions that are similar to experimentally obtained values of orientation.
Infusing theory into deep learning for interpretable reactivity prediction
Wang, Shih-Han; Pillai, Hemanth Somarajan; Wang, Siwen; Achenie, Luke E. K.; Xin, Hongliang (Nature Research, 2021)
Despite recent advances of data acquisition and algorithms development, machine learning (ML) faces tremendous challenges to being adopted in practical catalyst design, largely due to its limited generalizability and poor explainability. Herein, we develop a theory-infused neural network (TinNet) approach that integrates deep learning algorithms with the wellestablished d-band theory of chemisorption for reactivity prediction of transition-metal surfaces. With simple adsorbates (e.g., *OH, *O, and *N) at active site ensembles as representative descriptor species, we demonstrate that the TinNet is on par with purely data-driven ML methods in prediction performance while being inherently interpretable. Incorporation of scientific knowledge of physical interactions into learning from data sheds further light on the nature of chemical bonding and opens up new avenues for ML discovery of novel motifs with desired catalytic properties.
Inhibition of Heat Shock Protein 90 Reduces Inflammatory Signal Transduction in Murine J774 Macrophage Cells and Lessens Disease in Autoimmune MRL/lpr Mice: What in vitro, in vivo, and in silico Models Reveal
Shimp, Samuel Kline (Virginia Tech, 2012-04-30)
Heat shock protein 90 (HSP90) is a molecular chaperone protein that protects proteins from degradation, repairs damaged proteins, and assists proteins in carrying out their functions. HSP90 has hundreds of clients, many of which are inflammatory signaling kinases. The mechanism by which HSP90 enables inflammatory pathways is an active area of investigation. The HSP90 inhibitors such as geldanamycin (GA) and its derivative 17-dimethylaminoethylamino-17-demethoxygeldanamycin (17-DMAG) have been shown to reduce inflammation. It was hypothesized that inhibiting HSP90 would reduce inflammatory signal cascade levels. To test this, J774 mouse macrophage cells were treated with 17-DMAG and immune-stimulated with lipopolysaccharide (LPS). 17-DMAG treatment reduced nitric oxide (NO) production and the expression of pro-inflammatory cytokines interleukin (IL)-6, IL-12, and TNF-α. Inhibition of HSP90 also prevented nuclear translocation of NF-κB. To investigate the anti-inflammatory effects of HSP90 inhibition in vivo, MRL/lpr lupus mice were administered 5 mg/kg 17-DMAG for six weeks via intraperitoneal injection. Mice treated with 17-DMAG were found to have reduced proteinuria and reduced splenomegaly. Flow cytometric analysis of splenocytes showed that 17-DMAG decreased double negative T (DNT) cells. Renal expression of HSP90 was also measured and found to be increased in MRL/lpr mice that did not receive 17-DMAG. The mechanistic interactions between HSP90 and the pro-inflammatory nuclear factor-κB (NF-κB) pathway were studied and a computational model was developed. The model predicts cellular response of inhibitor of κB kinase (IKK) activation and NF-κB activation to LPS stimulation. Model parameters were fit to IKK activation data. Parameter sensitivity was assessed through simulation studies and showed a strong dependence on IKK-HSP90 binding. The model also accounts for the effect of a general HSP90 inhibitor to disrupt the IKK-HSP90 interaction for reduced activation of NF-κB. Model simulations were validated with experimental data. In conclusion, HSP90 facilitates inflammation through multiple signal pathways including Akt and IKK. Inhibition of HSP90 by 17-DMAG reduced disease in the MRL/lpr lupus mouse model. A computational model supported the hypothesis that HSP90 is required for IKK to activate the NF-κB pathway. Taken together, HSP90 is a prime target for therapeutic regulation of many inflammatory processes and warrants further study to understand its mechanism of regulating cell signaling cascades.

Browsing by Author "Achenie, Luke E. K."

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