Missouri University of Science and Technology
Recent publications
Nanoparticle-based therapeutics represent potential strategies for treating atherosclerosis; however, the complex plaque microenvironment poses a barrier for nanoparticles to target the dysfunctional cells. Here, we report reactive oxygen species (ROS)-responsive and size-reducible nanoassemblies, formed by multivalent host-guest interactions between β-cyclodextrins (β-CD)-anchored discoidal recombinant high-density lipoprotein (NP³ST) and hyaluronic acid-ferrocene (HA-Fc) conjugates. The HA-Fc/NP³ST nanoassemblies have extended blood circulation time, specifically accumulate in atherosclerotic plaque mediated by the HA receptors CD44 highly expressed in injured endothelium, rapidly disassemble in response to excess ROS in the intimal and release smaller NP³ST, allowing for further plaque penetration, macrophage-targeted cholesterol efflux and drug delivery. In vivo pharmacodynamicses in atherosclerotic mice shows that HA-Fc/NP³ST reduces plaque size by 53%, plaque lipid deposition by 63%, plaque macrophage content by 62% and local inflammatory factor level by 64% compared to the saline group. Meanwhile, HA-Fc/NP³ST alleviates systemic inflammation characterized by reduced serum inflammatory factor levels. Collectively, HA-Fc/NP³ST nanoassemblies with ROS-responsive and size-reducible properties exhibit a deeper penetration in atherosclerotic plaque and enhanced macrophage targeting ability, thus exerting effective cholesterol efflux and drug delivery for atherosclerosis therapy.
The catalytic combustion of H2/CO/O2/N2 mixtures over PdO was investigated at pressures 3 to 10 bar, H2:CO volumetric ratios 1:5 to 3:1, and global equivalence ratios φ = 0.13 and 0.23. The catalyst surface temperatures were controlled to 540–690 K, a range especially important for hybrid hetero-/homogeneous combustion approaches with large gas turbines at idle or part-load operation and for microreactors with recuperative small-scale turbines. In situ Raman measurements determined the major gas-phase species concentrations over the catalyst boundary layers in a channel-flow reactor, thermocouples monitored the surface temperatures, and surface characterization identified the catalyst oxidation state (PdO) and surface morphology. A 2-D CFD code with a detailed catalytic reaction mechanism simulated the experiments. Simulations and measurements of the combustion of the individual fuel components revealed pressure dependencies ∼p0.74 and ∼p0.10 for the CO and H2 reactivities, respectively, at the investigated equivalence ratios. In the combustion of H2/CO blends, transition temperatures (TTRAN) were identified, below (above) which H2 inhibited (promoted) chemically the oxidation of CO. The transition temperatures decreased with increasing H2:CO volumetric ratio, pressure, and equivalence ratio. Sensitivity analysis indicated that the H2 and O2 adsorption reactions had the larger inhibiting effect on CO oxidation, particularly at lower pressures. Comparisons with other noble metals showed that the PdO transition temperatures were higher than those on Pt and Rh. Even though this behavior favored Pt and Rh for the ignition of syngas in practical catalytic burners, the H2 and CO kinetic coupling (H2 inhibition) was considerably weaker on PdO at T < TTRAN, thus rendering PdO also potentially suitable for low temperature syngas ignition.
The catalytic (heterogeneous) and gas-phase (homogeneous) combustion of C3H8/O2/N2 mixtures over rhodium was investigated experimentally and numerically at 5 bar and at fuel-rich equivalence ratios φ = 2.0-3.5 relevant to propane Catalytic Partial Oxidation (CPO). In situ spatially-resolved Raman measurements of major gas-phase species concentrations and Planar Laser Induced Fluorescence (PLIF) of formaldehyde were applied in an optically accessible channel-flow reactor to monitor the catalytic and gas-phase processes, respectively, while accompanying 2D simulations were carried out with detailed hetero-/homogeneous chemical reaction mechanisms. Due to the high gas-phase reactivity of propane, homogeneous chemistry could not be ignored over most of the reactor's oxidation zone length (upstream zone where the deficient reactant oxygen is not fully consumed). The presence of gas-phase chemistry deteriorated the otherwise high catalytic syngas (H2 and CO) selectivities over the oxidation zone. Raman measurements of major gas-phase species concentrations over the restricted oxidation zone length without appreciable gas-phase chemistry showed that the catalytic reaction mechanism slightly underpredicted (overpredicted) the H2 (CO) formation. The same behavior was also attested over the remaining length of the oxidation zone where combined catalytic and gas-phase chemistry was present. The production of considerable amounts of H2 at the highest investigated equivalence ratio of 3.5 accelerated the onset of homogeneous ignition and the formation of strong flames. The discrepancies between measured and predicted homogeneous ignition distances were less than 6.8% in all cases, illustrating the validity of the employed hetero-/homogeneous kinetic schemes. Contrary to past methane CPO studies, the contribution of gas-phase chemistry and the formation of strong flames in propane CPO was detrimental to syngas production.
As one of the most widely used technology to ameliorate the reservoir's heterogeneity, polymer gels have been applied for more than 60 years. However, how to plug fractured reservoirs with significant abnormal features, high temperature and high salinity, especially the divalent cations, is still a challenging target. This work systematically evaluated a novel salt-resistant re-crosslinkable preformed particle gel (SR-RPPG) designed for fractured reservoirs with excellent salt resistance (up to 5 % CaCl2). We evaluated the swelling kinetics, thermal stability and plugging efficiency of this SR-RPPG. We assessed the swelling kinetic and re-crosslinking behavior of the SR-RPPG through the bottle test method. High temperature-resistant glass tubes with thermally stable O-rings were employed to evaluate the long-term thermal stability of the SR-RPPG product, and the testing lasted for over 200 days. A fractured model was used to assess the plugging efficiency of the SR-RPPG product. Results showed that the SR-RPPG could swell more than 30 times its original volume in 5 % CaCl2 and a middle east formation water. Besides, the SR-RPPG gel slurry can re-crosslink to form a rubber-like elastic bulk gel at 80–100 °C, and the elastic modulus of the re-crosslinked bulk gel can reach up to 1350 Pa with a swelling ratio of 10. The SR-RPPG prepared in 1 % NaCl, 2 % KCl, middle east formation water and 5 % CaCl2 with a swelling ratio of 10 have been stable for over 200 days at 100 °C. The core flooding test demonstrated that the SR-RPPG could efficiently block the open fractures, and the water breakthrough pressure gradient reached 927.30 psi/ft (20.98 MPa/m).
Metallic targets impacted by blunt-nosed projectiles typically fail via shear plugging. Various models exist that predict the onset of this failure threshold, which can be used to determine the ballistic limit velocity for a particular combination of projectiles and targets. In a previous study, nine existing penetration models were evaluated for their ability to predict the ballistic limit velocity of monolithic titanium alloy, aluminum alloy, and steel plates under small caliber fragment-simulating projectile impact. In a second study, a series of changes to these nine models were proposed, typically based on empirical adjustments, reformulation of the target strength dependency, or a combination of both. The effectiveness of these changes in improving the predictive capabilities of these nine models was assessed by comparing model predictions against more than 650 ballistic limit measurements. In this paper, we compare the ballistic limit velocities predicted by these nine models against ballistic limit measurements not included in the original 650 + dataset that guided the development of model improvements. It was found that the nine penetration models considered in these two previous studies are most suited for applications in which target plates can be considered “hard” or “high-strength.” In situations where target plates are made of “softer” materials, the predictive ability of these nine models was less than desirable.
Fibrous-type filters are used to capture dust particles in mining and other occupations where personnel are exposed for prolonged periods. Dust cleansing devices including flooded-bed dust scrubbers use these mesh-type multi-layered filters. These filters trap dust particles efficiently on their surface and inside their mesh. However, their continued operation leads to dust build-up and clogging. This results in increased resistance of the filter and lowered airflow rate through the scrubber. This could potentially enhance the exposure of the miners. A non-clogging self-cleaning impingement screen type dust filter was designed by the authors for use in mining and industrial dust cleansing applications. The filter guides dirt-laden air through rapidly turning paths which forces it to shed heavier particles. The particles impact one of the impermeable solid metallic filter surfaces and are removed from the airstream. A full cone water spray installed upstream prevents any surface build-up of dust. This paper summaried the computer models generated to show the filter operations and laboratory experiments including optical particle counting to establish the cleaning efficiency.
Network service provisioning becomes flexible and programmable with the help of Network Function Virtualization (NFV), since NFV abstracts various service functions into software components called Virtual Network Function (VNF) and VNFs can be flexibly and quickly composed to form new services. It is commonly known that sharing the same VNF among different services can improve the resource utilization. However, we should be aware that such sharing also leads to serious resource preemption. In addition, VNF sharing aggravates the generation of the performance bottleneck, which then causes the rate mismatch problem between the upstream and downstream VNFs belonging to the same service chain. In this work, we propose a dynamic and flexible algorithm to jointly address the VNF sharing resource allocation and the rate coordination between the upstream and downstream VNFs. Specifically, 1) the VNFs are shared among different service chains with a fairness factor considered for the purpose of reducing the resource preemption probability and improving the resource utilization; 2) the backpressure indicator of each VNF is defined to judge its pressure condition, based on which we can dynamically adjust the processing rates between it and its downstream or upstream VNFs by maximizing the idle resource utilization. The experimental results indicate that the proposed algorithm outperforms the other methods in terms of the average delay, the flow completion time, the throughput and the backlog, etc. Meanwhile, the proposed algorithm achieves more stable performance than the other methods.
Coal workers’ pneumoconiosis is one of the most severe occupational diseases due to long-term exposure to high concentrations of coal dust. Water spray with surfactant addition is an effective and commonly used method to control the coal dust in coal mines. Usually, the surfactant evaluation methods can be divided into two categories, including static and dynamic tests. The performance of surfactants may vary with different evaluation methods, which make the results hard to conclude. By critically reviewing previous studies, this paper highlighted the four surfactants’ evaluating methods for coal dust suppression, including the surface tension test, the sink test, the field test, and the wind tunnel test. Firstly, the basics of four evaluating methods were described, and the general result was concluded and presented. Secondly, static and dynamic tests were compared to reveal the consistency and discrepancy. It was found that the surface tension test takes dominations at lower concentrations of surfactants, while it cannot further take advantage after a critical concentration. The anionic and non-ionic surfactants showed better performance than cationic surfactants mostly in static tests. The field test and the wind tunnel test can directly evaluate the suppression efficiency of surfactants, with consideration of environmental factors such as the particle collision and the contact time. This paper is important for producing comparable results between static and dynamic tests and revealing the consistency and discrepancy. The outcomes of this study could be a guidance for evaluating and selecting the optimum surfactants for coal dust suppression.
Machine learning (ML) often requires large datasets for reliable predictions, which may not be feasible for most commercial alloy systems. Also, the alloy development requires a full set of balanced properties, many of which have not been thoroughly investigated by ML. In this study, we focused on the practicality and reliability of ML in estimating alloy properties with a realistic small dataset of commercial wrought aluminum alloys as an example. We have compiled a small but comprehensive dataset that contains 236 entries with 6 mechanical properties and 9 technological properties. We first performed statistical analysis to understand the encoded correlation among compositions, mechanical and technological properties. Then, we systematically evaluated the predictive performance of several popular ML models with a focus on the bias-variance trade-off, a central problem in training supervised ML models. Moreover, we looked into the prospect of improving ML models by engineering the feature space. Finally, our feature importance analysis suggested the soundness of the developed models and revealed new insights on the underlying composition/processing-property relations. This study demonstrated that alloy design may be aided by using machine learning and data mining techniques on realistic small datasets.
In this paper, an intelligent method for fault detection and classification for a microgrid (MG) was proposed. The idea was based on the combination of three computational tools: signal processing using the maximal overlap discrete wavelet packet transform (MODWPT), parameter optimization by the augmented Lagrangian particle swarm optimization (ALPSO), and machine learning using the support vector machine (SVM). The MODWPT was applied to preprocess half cycle of the post-fault current samples measured at both ends of feeders. The wavelet coefficients derived from the MODWPT were statistically evaluated using the mean, standard deviation, energy, skewness, kurtosis, logarithmic energy entropy, max, min, and Shannon entropy. These were the input feature datasets and were used to train the SVM classifier. The ALPSO was utilized to reduce the feature subsets and select the sensitive parameters of the SVM (i.e., penalty factor and the slack variable) to further improve the performance of the SVM. The intelligent relaying scheme was executed on a real-time digital simulator (RTDS) which is integrated with Matlab. The performance of SVM-based protection method is compared to several different protection models in terms of signal processing tools, optimization techniques used for selecting datasets and sensitive parameters, and classifiers under different operating conditions. Numerous operating conditions, including islanded or non-islanded operation modes and radial and or loop topologies introducing different characteristics of fault were included as the case studies for the proposed technique. A comprehensive evaluation study of the consortium for electric reliability technology solutions (CERTS) MG system and IEEE 34-bus confirms that the proposed protection scheme is accurate, fast, and robust to noisy measurements. In addition, the obtained results illustrate that the proposed method is superior to the recently published works in the literature.
Copper slag (CS), a by-product of copper smelting, is normally stockpiled, leading to wastes of resource and space as well as environment pollution. It has not been massively reutilized as a supplementary cementitious material in Portland cement due to its low reactivity. In the present study, CS is for the first time utilized as the base component to prepare an iron phosphate cement (IPC) by reacting with ammonium dihydrogen phosphate (ADP) at room temperature. The influence of the raw materials mass ratio (CS/ADP) on the microstructure and performance of IPC pastes are investigated. It is found that the compressive strength of IPC pastes at all ages is not a monotonic function of CS/ADP, and the paste with CS/ADP of 2.0 gives the highest strengths, i.e., 26.8, 38.9 and 47.5 MPa at 1, 3 and 28 d, respectively. The crystalline phases including FeH2P3O10·H2O and FePO4 are formed as the main reaction products to bind the unreacted CS particles. The early age hydration of IPC is found to be a multi-stage process, involving the initial dissolution of ADP and iron-containing phases of CS, the formation of FeH2P3O10·H2O, the initial generation of FePO4, and the attainment of the hydration reaction equilibrium. Unlike the magnesium phosphate cement, a redox reaction of Fe(Ⅱ) into Fe(Ⅲ) occurs due to the suitable range of pH and oxidation-reduction potential of the IPC system during the hydration reaction.
The use of alternative jet fuels by commercial aviation has increased substantially in recent years. Beside the reduction of carbon dioxide emission, the use of sustainable aviation fuels (SAF) may have a positive impact on the reduction of particulate emissions. This study summarizes the results from a ground-based measurement activity conducted in January 2018 as part of the ECLIF2/ND-MAX campaign in Ramstein, Germany. Two fossil reference kerosenes and three different blends with the renewable fuel component HEFA-SPK (Hydroprocessed Esters and Fatty Acids Synthetic Paraffinic Kerosene) were burned in an A320 with V2527-A5 engines to investigate the effect of fuel naphthalene/aromatic content and the corresponding fuel hydrogen content on non-volatile particle number and mass emissions. Reductions up to 70% in non-volatile particle mass emission compared to the fossil reference fuel were observed at low power settings. The reduction trends to decrease with increasing power settings. The fuels showed a decrease in particle emission with increasing fuel hydrogen content. Consequently, a second fossil fuel with similar hydrogen content as one of the HEFA blends featured similar reduction factors in particle mass and number. Changes in the fuel naphthalene content had significant impact on the particle number emission. A comparison to in-flight emission data shows similar trends at cruise altitudes. The measurements highlight the importance of individual fuel components in regulating engine emissions, particularly at the low thrust settings typically employed during ground operations (e.g. during idle and taxi). Therefore, when selecting and mixing SAF blends to meet present fuel-certification standards, attention should be paid to minimizing complex aromatic content to achieve the greatest possible air quality and climate benefits.
In the current wind loading codes for transmission towers, the wind loads under skewed winds are characterized by the global drag coefficient, skewed wind load factor, and wind load distribution factor. The recommended global drag coefficients in the codes borrowed from the lattice frames and trusses show discrepancy with the wind tunnel test. The skewed wind load factor reflecting the amplification of wind loads at skewed directions is demonstrated to vary with the tower geometry, which is not factored in the codes. In addition, the wind load distribution factor in the codes is determined by assuming that the wind load direction is the same as the wind direction, whose rationality needs to be examined. In this paper, a series of wind tunnel tests on the body, cross-arm and head sections of the widely used square angle-steel transmission towers are performed under multi-directional winds. Based on wind tunnel tests, the three coefficients and factors in the codes are extensively examined, and new formulas for them are calibrated. The results of this study could provide more accurate estimate of wind loads on the transmission towers.
Internal corrosion reduces the pipeline thickness and thus is one of the most predominant causes for pipeline malfunction. In this work, a highly sensitive, magnet-assisted hybrid sensor of fiber Bragg grating (FBG) and extrinsic Fabry-Perot interferometer (EFPI) is proposed, and designed for simultaneous measurement of temperature and pipeline thickness loss. The proposed sensor consists of a cylindrical magnet supported with springs, a reflection mirror fixed on the pipe for EFPI setup and a FBG for temperature measurement. The magnetic force between the magnet and the steel pipe is a function of the pipe wall thickness, which can be transferred to the cavity length (distance between the magnet and the reflection mirror) of the EFPI. The cavity length decreases with steel pipe corrosion due to the increase of the magnetic force. The proposed sensor has the sensitivity of 3.5 µm in thickness measurement and can be installed directly on the existed pipeline without interrupting its operation status.
Channel equalization is the efficient method for recovering distorted signal and correspondingly reducing bit error rate (BER). Different type of equalizations, like feed forward equalization (FFE) and decision feedback equalization (DFE) are canceling channel effect and recovering channel response. Separate optimization of tap coefficients for FFE and DFE does not give optimal result. In this case FFE and DFE tap coefficients are found separately and they are not collaborating. Therefore, the final equalization result is not global optimal. In the present paper new analytical method for finding best tap coefficients for FFE and DFE joint equalization is introduced. The proposed method can be used for both NRZ and PAM4 signals. The idea of the methodology is to combine FFE and DFE tap coefficients into one optimization problem and allow them to collaborate and lead to the global optimal solution. The proposed joint optimization method is fast, easy to implement and efficient. The method has been tested for several measured channels and the analysis of the results are discussed.
Isoatomic NiTi alloy (Nitinol) has become an important biomaterial due to its unique characteristics, including shape memory effect, superelasticity, and high damping. Nitinol has been widely used in the biomedical field, including orthopedics, vascular stents, orthodontics, and other medical devices. However, there have been convicting views about the bio-compatibility of Nitinol. Some studies have shown that Nitinol has extremely low cytotoxicity, indicating Nitinol has good biocompatibility. However, some studies have shown that the in-vivo corrosion resistance of Nitinol significantly decreases. This comprehensive paper discusses the historical developments of Nitinol, its biomedical applications, and its specific functional property. These render the suitability of Nitinol for such biomedical applications and provide insights into its in vivo and in vitro biocompatibility in the physiological environment and the antimicrobial strategies that can be applied to enhance its biocompatibility. Although 3D metal printing is still immature and Nitinol medical materials are difficult to be processed, Nitinol biomaterials have excellent potential and commercial value for 3D printing. However, there are still significant problems in the processing of Nitinol and improving its biocompatibility. With the deepening of research and continuous progress in surface modification and coating technology, a series of medical devices made from Nitinol are expected to be released soon.
The synthesis and crystallographic site occupancy were investigated for MgAl2O4 with and without mechanical activation of the precursor powders. Heating to 1200 °C or higher resulted in the formation of a single spinel phase regardless of whether the powders were mechanically activated or not. Neutron diffraction analysis was used to determine cation site occupancy and revealed that mechanical activation resulted in a lower degree of cation site inversion compared to the nonactivated materials, which indicated that the powders were closer to thermodynamic equilibrium. This is the first study to characterize the effects of mechanical activation on crystallographic site occupancy in magnesium aluminate spinel using neutron diffraction.
Advanced glycation end products (AGEs), formed through the nonenzymatic reaction of reducing sugars with the side-chain amino groups of lysine or arginine of proteins, followed by further glycoxidation reactions under oxidative stress conditions, are involved in the onset and exacerbation of a variety of diseases, including diabetes, atherosclerosis, and Alzheimer’s disease (AD) as well as in the secondary stages of traumatic brain injury (TBI). AGEs, in the form of intra- and interprotein crosslinks, deactivate various enzymes, exacerbating disease progression. The interactions of AGEs with the receptors for the AGEs (RAGE) also result in further downstream inflammatory cascade events. The overexpression of RAGE and the AGE-RAGE interactions are especially involved in cases of Alzheimer’s disease and other neurodegenerative diseases, including TBI and amyotrophic lateral sclerosis (ALS). Maillard reactions are also observed in the gut bacterial species. The protein aggregates found in the bacterial species resemble those of AD and Parkinson’s disease (PD), and AGE inhibitors increase the life span of the bacteria. Dietary AGEs alter the gut microbiota composition and elevate plasma glycosylation, thereby leading to systemic proinflammatory effects and endothelial dysfunction. There is emerging interest in developing AGE inhibitor and AGE breaker compounds to treat AGE-mediated pathologies, including diabetes and neurodegenerative diseases. Gut-microbiota-derived enzymes may also function as AGE-breaker biocatalysts. Thus, AGEs have a prominent role in the pathogenesis of various diseases, and the AGE inhibitor and AGE breaker approach may lead to novel therapeutic candidates.
A modern framework for assessing patient histories and conducting clinical research has been developed as the number of clinical narratives evolves. To discover the knowledge from such clinical narratives, clinical entity recognition and relation extraction tasks were performed subsequently in existing approaches, which resulted in error propagation. Therefore, a novel end-to-end clinical knowledge discovery strategy has been proposed in this paper. The clinical XLNet was used as a base model for handling the discrepancy issue. To predict the dependent clinical relation association, the multinomial Naïve Bayes probability function has been incorporated. In order to improve the performance of the proposed strategy, it takes into account entity pairs presented consecutively through the multi-head attention layer. Tests have been conducted using the N2C2 corpus, and the proposed methodology achieves a greater than 20% improvement in accuracy over existing neural network-based and transformer-based methods.
Smart solar roadways are an emerging cyber‐physical technology being developed by companies worldwide with some roadways even being in the early stages of implementation. These roadway systems have complex capabilities and connections with other systems. The recent and continuous advancements in solar technologies and wireless data transfer there exists a need to be able to evaluate the system of systems meta‐architecture for the development and implementation of smart solar roadways. Therefore, this paper outlines an example of the possibility for evaluation of the meta‐architectures using fuzzy logic assessors and genetic algorithms in conjunction with the SoS Explorer Application developed by the Engineering Management and Systems Engineering Department of Missouri University of Science and Technology. The optimal solution for the smart solar roadway maximizes each of the Key Performance Attributes (KPA) Affordability, Availability, Durability and Responsiveness. The KPAs can be assessed in a Fuzzy Inference System (FIS) to assess the fitness measure of the optimum SoS meta‐architecture generated by the genetic algorithm. The SoS Explorer Application is used to visualize the selected SoS meta‐architecture as a chromosome. Using this approach will allow local governments to make the optimal selection of systems to develop a smart solar roadway even as technology continues to progress.
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3,221 members
Abhijit Gosavi
  • Department of Engineering Management & Systems Engineering
Surender Maddela
  • Department of Material Science & Engineering
Steve A. Ndengué
  • Department of Chemistry
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