Showing papers by "Missouri University of Science and Technology published in 2017"

Dispersions of Two-Dimensional Titanium Carbide MXene in Organic Solvents

Abstract: Two-dimensional titanium carbide (Ti3C2Tx) MXene has attracted a great deal of attention in the research community and has already showed promise in numerous applications, but only its dispersions in aqueous solutions have previously been available. Here we show that Ti3C2Tx can be dispersed in many polar organic solvents, but the best dispersions were achieved in N,N-dimethylformamide, N-methyl-2-pyrrolidone, dimethyl sulfoxide, propylene carbonate, and ethanol. The dispersions were examined by measuring the concentration and absorbance spectra of MXene in organic solvents as well as correlating the concentration to solvent physical properties, such as surface tension, boiling point, and polarity index. Hildebrand and Hansen solubility parameters were additionally used to provide an initial understanding of how Ti3C2Tx MXene behaves in organic media and potentially develop quantitative correlations to select solvents and their combinations that can disperse Ti3C2Tx and other MXenes. Using this analysis, ...

Real-time monitoring of laser powder bed fusion process using high-speed X-ray imaging and diffraction

Abstract: We employ the high-speed synchrotron hard X-ray imaging and diffraction techniques to monitor the laser powder bed fusion (LPBF) process of Ti-6Al-4V in situ and in real time. We demonstrate that many scientifically and technologically significant phenomena in LPBF, including melt pool dynamics, powder ejection, rapid solidification, and phase transformation, can be probed with unprecedented spatial and temporal resolutions. In particular, the keyhole pore formation is experimentally revealed with high spatial and temporal resolutions. The solidification rate is quantitatively measured, and the slowly decrease in solidification rate during the relatively steady state could be a manifestation of the recalescence phenomenon. The high-speed diffraction enables a reasonable estimation of the cooling rate and phase transformation rate, and the diffusionless transformation from β to α ’ phase is evident. The data present here will facilitate the understanding of dynamics and kinetics in metal LPBF process, and the experiment platform established will undoubtedly become a new paradigm for future research and development of metal additive manufacturing.

Carbon Capture and Utilization Update

Abstract: In recent years, carbon capture and utilization (CCU) has been proposed as a potential technological solution to the problems of greenhouse-gas emissions and the ever-growing energy demand. To combat climate change and ocean acidification as a result of anthropogenic CO2 emissions, efforts have already been put forth to capture and sequester CO2 from large point sources, especially power plants; however, the utilization of CO2 as a feedstock to make valuable chemicals, materials, and transportation fuels is potentially more desirable and provides a better and long-term solution than sequestration. The products of CO2 utilization can supplement or replace chemical feedstocks in the fine chemicals, pharmaceutical, and polymer industries. In this review, we first provide an overview of the current status of CO2-capture technologies and their associated challenges and opportunities with respect to efficiency and economy followed by an overview of various carbon-utilization approaches. The current status of combined CO2 capture and utilization, as a novel efficient and cost-effective approach, is also briefly discussed. We summarize the main challenges associated with the design, development, and large-scale deployment of CO2 capture and utilization processes to provide a perspective and roadmap for the development of new technologies and opportunities to accelerate their scale-up in the near future.

iPrivacy: Image Privacy Protection by Identifying Sensitive Objects via Deep Multi-Task Learning

Abstract: To achieve automatic recommendation of privacy settings for image sharing, a new tool called iPrivacy (image privacy) is developed for releasing the burden from users on setting the privacy preferences when they share their images for special moments. Specifically, this paper consists of the following contributions: 1) massive social images and their privacy settings are leveraged to learn the object-privacy relatedness effectively and identify a set of privacy-sensitive object classes automatically; 2) a deep multi-task learning algorithm is developed to jointly learn more representative deep convolutional neural networks and more discriminative tree classifier, so that we can achieve fast and accurate detection of large numbers of privacy-sensitive object classes; 3) automatic recommendation of privacy settings for image sharing can be achieved by detecting the underlying privacy-sensitive objects from the images being shared, recognizing their classes, and identifying their privacy settings according to the object-privacy relatedness; and 4) one simple solution for image privacy protection is provided by blurring the privacy-sensitive objects automatically. We have conducted extensive experimental studies on real-world images and the results have demonstrated both the efficiency and effectiveness of our proposed approach.

Forecasting daily stock market return using dimensionality reduction

Abstract: A data mining procedure to forecast daily stock market return is proposed.The raw data includes 60 financial and economic features over a 10-year period.Combining ANNs with PCA gives slightly higher classification accuracy.Combining ANNs with PCA provides significantly higher risk-adjusted profits. In financial markets, it is both important and challenging to forecast the daily direction of the stock market return. Among the few studies that focus on predicting daily stock market returns, the data mining procedures utilized are either incomplete or inefficient, especially when a large amount of features are involved. This paper presents a complete and efficient data mining process to forecast the daily direction of the S&P 500 Index ETF (SPY) return based on 60 financial and economic features. Three mature dimensionality reduction techniques, including principal component analysis (PCA), fuzzy robust principal component analysis (FRPCA), and kernel-based principal component analysis (KPCA) are applied to the whole data set to simplify and rearrange the original data structure. Corresponding to different levels of the dimensionality reduction, twelve new data sets are generated from the entire cleaned data using each of the three different dimensionality reduction methods. Artificial neural networks (ANNs) are then used with the thirty-six transformed data sets for classification to forecast the daily direction of future market returns. Moreover, the three different dimensionality reduction methods are compared with respect to the natural data set. A group of hypothesis tests are then performed over the classification and simulation results to show that combining the ANNs with the PCA gives slightly higher classification accuracy than the other two combinations, and that the trading strategies guided by the comprehensive classification mining procedures based on PCA and ANNs gain significantly higher risk-adjusted profits than the comparison benchmarks, while also being slightly higher than those strategies guided by the forecasts based on the FRPCA and KPCA models.

Aerospace applications of laser additive manufacturing

Abstract: Additive manufacturing (AM) has been used in aerospace applications from the beginning. It not only plays a role as a rapid prototyping technology for saving capital and time during the product development period but also brings profound influences on product design, direct part fabrication, assembly, and repair in the aerospace industry. Because of recent developments, AM has rapidly become a strategic technology that will generate revenues throughout the aerospace supply chain. In this chapter, we analyze the characteristics of aerospace components favoring AM, discuss different aerospace applications benefit from different AM processes, and describe the repair applications for aerospace components. Examples of aerospace applications both from commercial and academia areas are also analyzed. Finally, this chapter discusses the challenges of applying AM to the aerospace industry and potential future aerospace applications.

Static and dynamic compressive properties of ultra-high performance concrete (UHPC) with hybrid steel fiber reinforcements

Abstract: Ultra-high performance concrete (UHPC) is promising in construction of concrete structures that suffer impact and explosive loads. In order to make UHPC structures more ductile and cost-effective, hybrid fiber reinforcements are often incorporated. In this study, a reference UHPC mixture with no fiber reinforcement and five mixtures with a single type of fiber reinforcement or hybrid fiber reinforcements of 6 and 13 mm in length at a total dosage of 2%, by the volume of concrete, were prepared. Quasi-static compressive and flexural properties of those mixtures were investigated. Split Hopkinson press bar (SHPB) testing was adopted to evaluate their dynamic compressive properties under three impact velocities. Test results indicated that UHPC with 1.5% long fiber reinforcements and 0.5% short fiber reinforcements demonstrated the best static and dynamic mechanical properties. The static compressive and flexural strengths of UHPC with 2% long fiber reinforcements were greater than those with 2% short fiber reinforcements, whereas comparable dynamic compressive properties were observed. Strain rate effect was observed for the dynamic compressive properties, including peak stress, dynamic increase factor, and absorbed energy. The reinforcing mechanisms of hybrid fiber reinforcements in UHPC were eventually discussed.

Powder characterisation techniques and effects of powder characteristics on part properties in powder-bed fusion processes

Abstract: Powder-bed fusion is a class of Additive Manufacturing (AM) processes that bond successive layers of powder to facilitate the creation of parts with complex geometries. As AM technology transitions from the fabrication of prototypes to end-use parts, the understanding of the powder properties needed to reliably produce parts of acceptable quality becomes critical. Consequently, this has led to the use of powder characterisation techniques such as scanning electron microscopy, laser light diffraction, X-ray photoelectron spectroscopy, and differential thermal analysis to study the effect of powder characteristics on part properties. Utilisation of these powder characterisation methods to study particle morphology, chemistry, and microstructure has resulted in significant strides being made towards the optimisation of powder properties. This paper reviews methods commonly used in characterising AM powders, and the effects of powder characteristics on the part properties in powder-bed fusion processes.

The Toxicity of Nanoparticles Depends on Multiple Molecular and Physicochemical Mechanisms.

Abstract: Nanotechnology is an emerging discipline that studies matters at the nanoscale level. Eventually, the goal is to manipulate matters at the atomic level to serve mankind. One growing area in nanotechnology is biomedical applications, which involve disease management and the discovery of basic biological principles. In this review, we discuss characteristics of nanomaterials, with an emphasis on transition metal oxide nanoparticles that influence cytotoxicity. Identification of those properties may lead to the design of more efficient and safer nanosized products for various industrial purposes and provide guidance for assessment of human and environmental health risk. We then investigate biochemical and molecular mechanisms of cytotoxicity that include oxidative stress-induced cellular events and alteration of the pathways pertaining to intracellular calcium homeostasis. All the stresses lead to cell injuries and death. Furthermore, as exposure to nanoparticles results in deregulation of the cell cycle (i.e., interfering with cell proliferation), the change in cell number is a function of cell killing and the suppression of cell proliferation. Collectively, the review article provides insights into the complexity of nanotoxicology.

Biomedical applications of nanodiamond (Review).

Abstract: The interest to applications of nanodiamond in biology and medicine is on the rise over the recent years. This is due to the unique combination of properties that nanodiamond provides. Small size (~ 5 nm), low cost, scalable production, negligible toxicity, chemical inertness of diamond core and rich chemistry of nanodiamond surface, as well as bright and robust fluorescence resistant to photobleaching are the distinct parameters that render nanodiamond superior to any other nanomaterial when it comes to biomedical applications. The most exciting recent results have been related to the use of nanodiamonds for drug delivery and diagnostics – two components of quickly growing area of biomedical research dubbed theranostics. However, nanodiamond offers much more in addition: it can be used to produce biodegradable bone surgery devices, tissue engineering scaffolds, kill drug resistant microbes, help us to fight viruses, and deliver genetic material into cell nucleus. All these exciting opportunities require an in-depth understanding of nanodiamond. This review covers the recent progress as well as general trends in biomedical applications of nanodiamond, and underlines the importance of purification, characterization, and rational modification of this nanomaterial when designing nanodiamond based theranostic platforms.

Unmanned Aerial System (UAS)-Based Phenotyping of Soybean using Multi-sensor Data Fusion and Extreme Learning Machine

Abstract: Estimating crop biophysical and biochemical parameters with high accuracy at low-cost is imperative for high-throughput phenotyping in precision agriculture. Although fusion of data from multiple sensors is a common application in remote sensing, less is known on the contribution of low-cost RGB, multispectral and thermal sensors to rapid crop phenotyping. This is due to the fact that (1) simultaneous collection of multi-sensor data using satellites are rare and (2) multi-sensor data collected during a single flight have not been accessible until recent developments in Unmanned Aerial Systems (UASs) and UAS-friendly sensors that allow efficient information fusion. The objective of this study was to evaluate the power of high spatial resolution RGB, multispectral and thermal data fusion to estimate soybean (Glycine max) biochemical parameters including chlorophyll content and nitrogen concentration, and biophysical parameters including Leaf Area Index (LAI), above ground fresh and dry biomass. Multiple low-cost sensors integrated on UASs were used to collect RGB, multispectral, and thermal images throughout the growing season at a site established near Columbia, Missouri, USA. From these images, vegetation indices were extracted, a Crop Surface Model (CSM) was advanced, and a model to extract the vegetation fraction was developed. Then, spectral indices/features were combined to model and predict crop biophysical and biochemical parameters using Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Extreme Learning Machine based Regression (ELR) techniques. Results showed that: (1) For biochemical variable estimation, multispectral and thermal data fusion provided the best estimate for nitrogen concentration and chlorophyll (Chl) a content (RMSE of 9.9% and 17.1%, respectively) and RGB color information based indices and multispectral data fusion exhibited the largest RMSE 22.6%; the highest accuracy for Chl a + b content estimation was obtained by fusion of information from all three sensors with an RMSE of 11.6%. (2) Among the plant biophysical variables, LAI was best predicted by RGB and thermal data fusion while multispectral and thermal data fusion was found to be best for biomass estimation. (3) For estimation of the above mentioned plant traits of soybean from multi-sensor data fusion, ELR yields promising results compared to PLSR and SVR in this study. This research indicates that fusion of low-cost multiple sensor data within a machine learning framework can provide relatively accurate estimation of plant traits and provide valuable insight for high spatial precision in agriculture and plant stress assessment.

Survey of Security Advances in Smart Grid: A Data Driven Approach

Abstract: With the integration of advanced computing and communication technologies, smart grid is considered as the next-generation power system, which promises self healing, resilience, sustainability, and efficiency to the energy critical infrastructure. The smart grid innovation brings enormous challenges and initiatives across both industry and academia, in which the security issue emerges to be a critical concern. In this paper, we present a survey of recent security advances in smart grid, by a data driven approach. Compared with existing related works, our survey is centered around the security vulnerabilities and solutions within the entire lifecycle of smart grid data, which are systematically decomposed into four sequential stages: 1) data generation; 2) data acquisition; 3) data storage; and 4) data processing. Moreover, we further review the security analytics in smart grid, which employs data analytics to ensure smart grid security. Finally, an effort to shed light on potential future research concludes this paper.

Polymer Gel Systems for Water Management in High-Temperature Petroleum Reservoirs: A Chemical Review

Abstract: Polymer gel systems as water management materials have been widely used in recent years for enhanced oil recovery applications. However, most polymer gel systems are limited in their ability to withstand the harsh environments of high temperature and high salinity. Those polymer gel systems that can handle high-temperature excessive water treatments are reviewed in this paper and categorized into three major types: in situ cross-linked polymer gels, preformed gels, and foamed gels. Future directions for the development of polymer gel systems for high-temperature conditions are recommended. For excessive water management with temperatures from 80 to 120 °C, current polymer systems are substantially adequate. Polymer gel systems composed of partially hydrolyzed polyacrylamide (HPAM)/chromium can be combined with nanoparticle technology to elongate their gelation time and reduce the adsorption of chromium ions in the formation. Phenolic resin cross-linker systems have reasonable gelation times and gel streng...

Optimization and performance of cost-effective ultra-high performance concrete

Abstract: This paper presents a mix design method for ultra-high performance concrete (UHPC) prepared with high-volume supplementary cementitious materials and conventional concrete sand. The method involves the optimization of binder combinations to enhance packing density, compressive strength, and rheological properties. The water-to-cementitious materials ratio is then determined for pastes prepared with the selected binders. The sand gradation is optimized using the modified Andreasen and Andersen packing model to achieve maximum packing density. The binder-to-sand volume ratio is then determined based on the void content, required lubrication paste volume, and compressive strength. The optimum fiber volume is selected based on flowability and flexural performance. The high-range water reducer dosage and w/cm are then adjusted according to the targeted mini-slump flow and compressive strength. Finally, the optimized UHPC mix designs are evaluated to determine key properties that are relevant to the intended application. This mix design approach was applied to develop cost-effective UHPC materials. The results indicate that the optimized UHPC can develop 28-days compressive strength of 125 MPa under standard curing condition and 168–178 MPa by heat curing for 1 days Such mixtures have unit cost per compressive strength at 28 days of 4.1–4.5 $/m3/MPa under standard curing.

MoZo: A Moving Zone Based Routing Protocol Using Pure V2V Communication in VANETs

Abstract: Vehicular Ad-hoc Networks (VANETs) are an emerging field, whereby vehicle-to-vehicle communications can enable many new applications such as safety and entertainment services. Most VANET applications are enabled by different routing protocols. The design of such routing protocols, however, is quite challenging due to the dynamic nature of nodes (vehicles) in VANETs. To exploit the unique characteristics of VANET nodes, we design a moving-zone based architecture in which vehicles collaborate with one another to form dynamic moving zones so as to facilitate information dissemination. We propose a novel approach that introduces moving object modeling and indexing techniques from the theory of large moving object databases into the design of VANET routing protocols. The results of extensive simulation studies carried out on real road maps demonstrate the superiority of our approach compared with both clustering and non-clustering based routing protocols.

3D-Printed Metal–Organic Framework Monoliths for Gas Adsorption Processes

Abstract: Metal–organic frameworks (MOFs) have shown promising performance in separation, adsorption, reaction, and storage of various industrial gases; however, their large-scale applications have been hampered by the lack of a proper strategy to formulate them into scalable gas–solid contactors. Herein, we report the fabrication of MOF monoliths using the 3D printing technique and evaluation of their adsorptive performance in CO2 removal from air. The 3D-printed MOF-74(Ni) and UTSA-16(Co) monoliths with MOF loadings as high as 80 and 85 wt %, respectively, were developed, and their physical and structural properties were characterized and compared with those of MOF powders. Our adsorption experiments showed that, upon exposure to 5000 ppm (0.5%) CO2 at 25 °C, the MOF-74(Ni) and UTSA-16(Co) monoliths can adsorb CO2 with uptake capacities of 1.35 and 1.31 mmol/g, respectively, which are 79% and 87% of the capacities of their MOF analogues under the same conditions. Furthermore, a stable performance was obtained for...

PlantCV v2: Image analysis software for high-throughput plant phenotyping

Abstract: Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here we present the details and rationale for major developments in the second major release of PlantCV. In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning.

Progress of microalgae biofuel’s commercialization

Abstract: Algae is a potent renewable source with the favorable characteristics. However, there are still many barriers in the related theory, techniques and industrialization, which lead to the high cost of the algae biofuel. This paper reviewed the process of the microalgae biofuel's commercial process. Investigated the barriers of the technologies especially the energy-extensive part and the pilot scale test which is the crucial part of the process. The policy supports of American, EU and China for microalgae biofuel industry and the effect of them was summarized at this paper. Numbers of pilot scale program has launched in the support of the government and the private investment, while there is still some distance from scale up production. Algae fundamental biology research, co-products’ production to make profits in short term, and support from government are key strategies of algae commercialization.

Improving flexural performance of ultra-high-performance concrete by rheology control of suspending mortar

Abstract: This study develops a rheology control method to improve steel fiber distribution and flexural performance of ultra-high-performance concrete (UHPC) by adjusting the rheological properties of the suspending mortar of UHPC before steel fibers are added. Correlations among the plastic viscosity of the suspending mortar, the resulting steel fiber distribution, and flexural properties of UHPC are established. This was done by changing the dosage of viscosity modified admixture (VMA) for investigated UHPC mixtures. The optimal plastic viscosity of the suspending mortar that allows for the optimized fiber distribution and flexural performance of UHPC is determined. The plastic viscosity is correlated with the mini V-funnel flow time, which provides a simple alternative to evaluate the plastic viscosity. For a UHPC mixture with 2% micro steel fibers, by volume, the optimal mini V-funnel flow time of suspending motar was determined to be 46 ± 2 s, which corresponded to the optimal plastic viscosity (53 ± 3 Pa s) that ensures the greatest fiber dispersion uniformity and flexural performance of UHPC. However, increasing the VMA dosage retarded the hydration kinetics and reduced the degree of hydration, compressive strength, and the bond properties of the fiber-matrix interface of UHPC.

Alkylammonium Cation Intercalation into Ti3C2 (MXene): Effects on Properties and Ion-Exchange Capacity Estimation

Abstract: Ti3C2Tx MXene intercalated with Li+ ions was produced and ion-exchanged with a series of trimethylalkylammonium (AA) cations of increasing alkyl chain length. A discontinuous expansion in the MXene layer spacing was observed, attributed to complete packing of the interlayer space at a critical chain length. The latter was used to estimate the number of cations per Ti3C2 formula unit, which was found to be in good agreement with a similar quantification obtained from X-ray photoelectron spectroscopy, energy-dispersive spectroscopy, and elemental analysis. The system was also modeled using density functional theory and molecular dynamics, arriving at cation concentrations in the same range. The intercalated AA cations led to tunable increases in resistivity of the normally highly electrically conductive MXene and were investigated as interlayer pillars in electrochemical capacitors.

A High-Voltage-Gain DC–DC Converter Based on Modified Dickson Charge Pump Voltage Multiplier

Abstract: A high-voltage-gain dc–dc converter is introduced in this paper. The proposed converter resembles a two-phase interleaved boost converter on its input side while having a Dickson-charge-pump-based voltage multiplier (VM) on its output side. This converter offers continuous input current, which makes it more appealing for the integration of renewable sources like solar panels to a 400-V dc bus. Also, the proposed converter is capable of drawing power from either a single source or two independent sources. Furthermore, the VM used offers low voltage ratings for capacitors that potentially leads to size reduction. The converter design and component selection have been discussed in detail with supporting simulation results. A hardware prototype of the proposed converter with ${V}_{\rm{in}}= \rm{ 20}$ and ${V}_{\rm{out}}= \rm{ 400}$ V has been developed to validate the analytical results.

Highly active and stable alumina supported nickel nanoparticle catalysts for dry reforming of methane

Abstract: A highly stable and extremely active nickel (Ni) nanoparticle catalyst, supported on porous γ-Al2O3 particles, was prepared by atomic layer deposition (ALD). The catalyst was employed to catalyze the reaction of dry reforming of methane (DRM). The catalyst initially gave a low conversion at 850 °C, but the conversion increased with an increase in reaction time, and stabilized at 93% (1730 L h−1 g Ni−1 at 850 °C). After regeneration, the catalyst showed a very high methane reforming rate (1840 L h−1 g Ni−1 at 850 °C). The activated catalyst showed exceptionally high catalytic activity and excellent stability of DRM reaction in over 300 h at temperatures that ranged from 700 °C to 850 °C. The excellent stability of the catalyst resulted from the formation of NiAl2O4 spinel. The high catalytic activity was due to the high dispersion of Ni nanoparticles deposited by ALD and the reduction of NiAl2O4 spinel to Ni during the DRM reaction at 850 °C. It was verified that NiAl2O4 can be reduced to Ni in a reductive gas mixture (i.e., carbon monoxide and hydrogen) during the reaction at 850 °C, but not by H2 alone.