Structure-Activity Relationship of Porous Materials for Energy Conversion and Storage

The design and synthesis of porous materials are of key importance in energy conversion and storage, due to their structure-related properties in the isolation of active materials and exploration of large active site. In this thesis, the recent process in synthesis and energy-related applications of porous carbon and noble metal materials have been carefully reviewed. And three porous materials have been used as catalyst support, host materials and active sites for biomass conversion, electrolytic hydrogen evolution, lithium-sulfur battery and oxygen reduction reaction. I found that the nanostructures of porous materials can significantly affect their activity and stability by either confining active sites, boosting mass transfer, stabilizing active materials or exploring high active surface area (Figure II-1). The structure-activity relationship is used across the entire thesis, which could give an insightful understanding of the activity and stability of designed catalysts. A liquid-free synthesis of porous carbon is developed to uses gases instead of liquid to disperse carbon precursor, leach templates and remove impurities, minimizing synthetic procedures and the use of chemicals. Therefore, the effects of pore geometries in catalysis can be isolated and investigated. Two of the resulted materials with different pore geometries are studied as supports for Ru clusters in the hydrogenolysis of 5-hydroxymethylfurfural (HMF) and electrochemical hydrogen evolution reaction (HER). The results showed catalysts supported by porous carbon with bottle-beck showed benchmark activity for hydrogenolysis of HMF due to the confinement of active Ru species, while tubular pores boost charge transfer and achieve high performance in HER. In order to further investigate the effect of hollow structure on sulfur stabilization and lithium polysulfides immobilization in cathode of lithium-sulfur battery, X-ray computed tomography (X-ray CT) was applied to provide direct visualization of microstructural evolution in cathode. The results show that the combination of hollow structure and nanosized adsorbent could significantly inhibit volume expansion and reduce the polysulfides shuttle effect, thereby enhance the electrochemical performance. In addition to catalyst support and host of active materials, use of porous materials as active sites for oxygen reduction reaction (ORR) is also investigated in this thesis. A novel catalyst design of large and porous PdPt particles that are tightly attached to the carbon support and exhibit excellent catalytic performance during the oxygen reduction reaction. From catalyst characterization and theory investigation, the origin of high activity and stability of this catalyst stem from its porous morphology, the strong interaction between the particle with the carbon support and the stabilization effect of Pd to Pt

Measurement of Reinforcement Corrosion in Concrete Adopting Ultrasonic Tests and Artificial Neural Network

Limited research has been performed in testing and measuring the reinforcement corrosion levels using non-destructive tests. This research applied ultrasonic-based non-destructive test and artificial neural network to the diagnosis and prediction of rebar’s non-uniform corrosion-induced damage within reinforced concrete members. Ultrasonic velocities were tested by applying ultrasonic to reinforced concrete prisms before and after the rebar corrosion. Input parameters including concrete strength, ultrasonic velocity, and the specimen dimension-related variable were used for the prediction of reinforcement corrosion level adopting artificial neural network models. Using totally 50 experimental observations, Radial Basis Function-based model was found with higher accuracy in predicting corrosion levels compared to Back Propagation-based model. This study leads to future research in high-accuracy non-destructive measurement of reinforcement corrosion in concrete

A Manipulator-Assisted Multiple UAV Landing System for USV Subject to Disturbance

Marine waves significantly disturb the unmanned surface vehicle (USV) motion. An unmanned aerial vehicle (UAV) can hardly land on a USV that undergoes irregular motion. An oversized landing platform is usually necessary to guarantee the landing safety, which limits the number of UAVs that can be carried. We propose a landing system assisted by tether and robot manipulation. The system can land multiple UAVs without increasing the USV's size. An MPC controller stabilizes the end-effector and tracks the UAVs, and an adaptive estimator addresses the disturbance caused by the base motion. The working strategy of the system is designed to plan the motion of each device. We have validated the manipulator controller through simulations and well-controlled indoor experiments. During the field tests, the proposed system caught and placed the UAVs when the disturbed USV roll range was approximately 12 degrees

Weakly Supervised Deep Learning for Thoracic Disease Classification and Localization on Chest X-rays

Chest X-rays is one of the most commonly available and affordable radiological examinations in clinical practice. While detecting thoracic diseases on chest X-rays is still a challenging task for machine intelligence, due to 1) the highly varied appearance of lesion areas on X-rays from patients of different thoracic disease and 2) the shortage of accurate pixel-level annotations by radiologists for model training. Existing machine learning methods are unable to deal with the challenge that thoracic diseases usually happen in localized disease-specific areas. In this article, we propose a weakly supervised deep learning framework equipped with squeeze-and-excitation blocks, multi-map transfer, and max-min pooling for classifying thoracic diseases as well as localizing suspicious lesion regions. The comprehensive experiments and discussions are performed on the ChestX-ray14 dataset. Both numerical and visual results have demonstrated the effectiveness of the proposed model and its better performance against the state-of-the-art pipelines.Comment: 10 pages. Accepted by the ACM BCB 201

A nearest level PWM method for the MMC in DC distribution grids

For modular multilevel converters (MMCs) applied to medium-voltage DC distribution grids, using the traditional Nearest Level Modulation (NLM) as in HVDC systems can lead to severe current distortion due to significantly reduced module number. This paper proposes a hybrid modulation method combining NLM and Pulse Width Modulation (PWM) where only one module per arm operates under PWM mode. The proposed Nearest Level PWM (NL-PWM) method not only significantly reduces the current distortion, but also avoids the complicated voltage balancing control in each module. The harmonic characteristics of NL-PWM are derived using double Fourier transform, which provides theoretical basis for selecting module number and switching frequency for medium-voltage application in accordance with grid harmonic requirements. Finally, the harmonic characteristics and feasibility of the proposed modulation method are validated by simulation and experimental studies on a MMC with 6 modules per arm. The simulated and experimental results reveal that NL-PWM has better voltage and current harmonic characteristics over NLM and CPS-PWM, thereby suiting the application of MMC with few models

State of Charge Estimation for Lithium-Ion Battery by Using Dual Square Root Cubature Kalman Filter

The state of charge (SOC) plays an important role in battery management systems (BMS). However, SOC cannot be measured directly and an accurate state estimation is difficult to obtain due to the nonlinear battery characteristics. In this paper, a method of SOC estimation with parameter updating by using the dual square root cubature Kalman filter (DSRCKF) is proposed. The proposed method has been validated experimentally and the results are compared with dual extended Kalman filter (DEKF) and dual square root unscented Kalman filter (DSRUKF) methods. Experimental results have shown that the proposed method has the most balance performance among them in terms of the SOC estimation accuracy, execution time, and convergence rate