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Browsing by Author "Qian, Chen"

Now showing 1 - 5 of 5
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    Adsorption of Xyloglucan onto Cellulose and Cellulase onto Self-assembled Monolayers
    Qian, Chen (Virginia Tech, 2012-04-19)
    Adsorption of xyloglucan (XG) onto thin desulfated nanocrystalline cellulose (DNC) films was studied by surface plasmon resonance spectroscopy (SPR), quartz crystal microbalance with dissipation monitoring (QCM-D), and atomic force microscopy (AFM) measurements. These studies were compared to adsorption studies of XG onto thin sulfated nanocrystalline cellulose (SNC) films and regenerated cellulose (RC) films performed by others. Collectively, these studies show the accessible surface area is the key factor for the differences in surface concentrations observed for XG adsorbed onto the three cellulose surfaces. XG penetrated into the porous nanocrystalline cellulose films. In contrast, XG was confined to the surfaces of the smooth, non-porous RC films. Surprisingly surface charge and cellulose morphology played a limited role on XG adsorption. The effect of the non-ionic surfactant Tween 80 on the adsorption of cellulase onto alkane thiol self-assembled monolayers (SAMs) on gold was also studied. Methyl (-CH3), hydroxyl (-OH) and carboxyl (-COOH) terminated SAMs were prepared. Adsorption of cellulase onto untreated and Tween 80-treated SAMs were monitored by SPR, QCM-D and AFM. The results indicated cellulase adsorption onto SAM-CH3 and SAM-COOH were driven by strong hydrophobic and electrostatic interactions, however, hydrogen bonding between cellulase and SAM-OH was weak. Tween 80 effectively hindered the adsorption of cellulase onto hydrophobic SAM-CH3 substrates. In contrast, it had almost no effect on the adsorption of cellulase onto SAM-OH and SAM-COOH substrates because of its reversible adsorption on these substrates.
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    Adsorption of Xyloglucan onto Thin Films of Cellulose Nanocrystals and Amorphous Cellulose: Film Thickness Effects
    Kittle, Joshua D.; Qian, Chen; Edgar, Emma; Roman, Maren; Esker, Alan R. (American Chemical Society, 2018-10-01)
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    Driving Risk Assessment Based on High-frequency, High-resolution Telematics Data
    Guo, Feng; Qian, Chen; Shi, Liang (SAFE-D: Safety Through Disruption National University Transportation Center, 2022-04)
    The emerging connected vehicle and Automated Driving System (ADS), the widely available advanced in-vehicle telematics data collection/transmitting systems, as well as smartphone apps produce gigantic amount of high-frequency, high-resolution driving data. These telematics data provide comprehensive information on driving style, driving environment, road condition, and vehicle conditions. The high frequency telematics data has been used for several safety areas such as insurance pricing, teenage driving risk evaluation, and fleet safety management. This report study advances traffic safety analysis in the follow aspects: 1) characterize the high-frequency kinematic signatures for safety critical events compared to normal operations; and 2) develop models to distinguish and predict crashes from normal driving scenarios based on the high frequency data. Two deep learning models were developed. The first one combines the strength of convolutional neural network (CNN), gated recurrent unit (GRU) network and extreme gradient boosting (XGBoost). The second approach is based on a novel variational inference for extremes (VIE) to address the rarity of crashes. The models proposed in this project can benefit a variety of traffic research and applications including connected vehicles and ADS real-time safety monitoring, NDS data analysis, ride-hailing safety prediction, as well as fleet and driver safety management programs.
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    Linking the Rheological Behavior to the Processing of Thermotropic Liquid Crystalline Polymers in the Super-cooled State
    Qian, Chen (Virginia Tech, 2016-06-01)
    Thermotropic liquid crystalline polymers (TLCPs) have attracted great interest because of the combination of their promising properties, which includes high stiffness and strength, excellent processability, and outstanding chemical resistance. TLCPs exhibit inherently low viscosity relative to many other conventional thermoplastics. The low melt viscosity is detrimental to processes requiring high melt strength, such as extrusion blow molding, film blowing, thermoforming and multilayer coextrusion. Our laboratory has developed a unique method to increase the viscosity of TLCPs by first raising the temperature above the melting point (Tm) to exclude all solid crystalline structure, and then lowering the temperature below Tm to super cool the materials. Additionally, the super-cooling behavior of TLCPs allows them to be blended with other thermoplastics possessing lower processing temperatures. The initial focus of this dissertation is to investigate the processing temperature of a representative TLCP in the super-cooled state, using the methods of small amplitude oscillatory shear (SAOS), the startup of shear flow and differential scanning calorimetry (DSC). The TLCP used in this work is synthesized from 4-hydroxybenzoic acid (HBA), terephthalic acid (TA), hydroquinone (HQ) and hydroquinone derivatives (HQ-derivatives). The TLCP of HBA/TA/HQ/HQ-derivatives has a melting point, Tm, of around 280 oC. Once melted, the TLCP can be cooled 30 oC below the Tm while still maintaining its processability. As the TLCP was cooled to 250 oC, a one order magnitude increase in viscosity was obtained at a shear rate of 0.1 s- 1. Additionally, super cooling the TLCP did not significantly affect the relaxation of shear stress after preshearing. However, the recovery of the transient shear stress in the interrupted shear measurements was suppressed to a great extent in the super-cooled state. The second part of this work is concerned with the extrusion blow molding of polymeric blends containing the TLCP of HBA/TA/HQ/HQ-derivatives and high density polyethylene (HDPE), using a single screw extruder. The blends were processed at a temperature of 260 oC which is 20 oC below Tm of the TLCP such that the thermal degradation of HDPE was minimized. Bottles were successfully produced from the blends containing 10, 20 and 50 wt% TLCP. The TLCP/HDPE blend bottles exhibited an enhanced modulus relative to pure HDPE. However, the improvement in tensile strength was marginal. At 10 and 20 wt% TLCP contents, the TLCP phase existed as platelets, which aligned along the machine direction. A co-continuous morphology was observed for the blend containing 50 wt% TLCP. The preliminary effectiveness of maleic anhydride grafted HDPE (MA-g-HDPE) as a compatibilizer for the TLCP/HDPE system was also studied. The injection molded ternary TLCP/HDPE/MA-g-HDPE blends demonstrated superior mechanical properties over the binary TLCP/HDPE blends, especially in tensile strength. Consequently, it is promising to apply the ternary blends of TLCP/HDPE/MA-g-HDPE in the blow molding process for improved mechanical properties. Finally, this work tends to determine how the isothermal crystallization behavior of a TLCP can be adjusted by blending it with another TLCP of lower melting point. One TLCP (Tm~350 oC) used is a copolyester of HBA/TA/HQ/HQ-derivatives with high HBA content. The other TLCP (Tm~280 oC) is a copolyesteramide of 60 mol% hydroxynaphthoic acid, 20 mol% terephthalic acid and 20 mol% 4-aminophenol. The TLCP/TLCP blends and neat TLCPs were first melted well above their melting points, then cooled to the predetermined temperatures below the melting temperatures at 10 oC/min to monitor the isothermal crystallization. As the content of the low melting TLCP increased in the blends, the temperature at which isothermal crystallization occurred decreased. Comparing with neat TLCPs, the blend of 75% low melting TLCP crystallized at a lower temperature than the pure matrices, and the blend remained as a stable super-cooled fluid in the temperature range from 220 to 280 oC. Under isothermal conditions, differential scanning calorimetry (DSC) was not capable of reliably detecting the the low energy released in the initial stage of crystallization. In contrast, small amplitude oscillatory shear (SAOS) was more sensitive to detecting isothermal crystallization than DSC.
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    Statistical learning for cyber physical system
    Qian, Chen (Virginia Tech, 2024-07-29)
    Cyber-Physical Systems represent a critical intersection of physical infrastructure and digital technologies. Ensuring the safety and reliability of these interconnected systems is vital for mitigating risks and enhancing overall system safety. In recent decades, the transportation domain has seen significant adoption of cyber-physical systems, such as automated vehicles. This dissertation will focus on the application of cyber-physical systems in transportation. Statistical learning techniques offer a powerful approach to analyzing complex transportation data, providing insights that enhance safety measures and operational efficiencies. This dissertation underscores the pivotal role of statistical learning in advancing safety within cyber physical transportation systems. By harnessing the power of data-driven insights, predictive modeling, and advanced analytics, this research contributes to the development of smarter, safer, and more resilient transportation systems. Chapter 2 proposes a novel stochastic jump-based model to capture the driving dynamics of safety-critical events. The identification of such events is challenging due to their complex nature and the high frequency kinematic data generated by modern data acquisition systems. This chapter addresses these challenges by developing a model that effectively represents the stochastic nature of driving behaviors and assume the happening of a jump process will lead to safety-critical situations. To tackle the issue of rarity in crash data, Chapter 3 introduces a variational inference of extremes approach based on a generalized additive neural network. This method leverages statistical learning to infer the distribution of extreme events, allowing for better generalization ability to unseen data despite the limited availability of crash events. By focusing on extreme value theory, this chapter enhances statistical learning's ability to predict and understand rare but high-impact events. Chapter 4 shifts focus to the safety validation of cyber-physical transportation systems, requiring a unique approach due to their advanced and complex nature. This chapter proposes a kernel-based method that simultaneously satisfies representativeness and criticality for safety verification. This method ensures that the safety evaluation process covers a wide range of scenarios while focusing on those most likely to lead to critical outcomes. In Chapter 5, a deep generative model is proposed to identify the boundary of safety-critical events. This model uses the encoder component to reduce high-dimensional input data into lower-dimensional latent representations, which are then utilized to generate new driving scenarios and predict their associated risks. The decoder component reconstructs the original high-dimensional case parameters, allowing for a comprehensive understanding of the factors contributing to safety-critical events. The chapter also introduces an adversarial perturbation approach to efficiently determine the boundary of risk, significantly reducing computational time while maintaining precision. Overall, this dissertation demonstrates the potential of using advanced statistical learning methods to enhance the safety and reliability of cyber-physical transportation systems. By developing innovative models and methodologies, this dissertation provides valuable tools and theoretical foundations for risk prediction, safety validation, and proactive management of transportation systems in an increasingly digital and interconnected world.