DEVELOPMENT OF FUNCTIONAL METAL OXIDE THIN FILMS VIA HIGHTHROUGHPUT PULSED LASER DEPOSITION FOR ADVANCED ENERGY APPLICATIONS

High-throughput methodologies are effective for rapid exploration of new materials with enhanced physical properties. In this thesis, we combine highthroughput pulsed laser deposition (HT-PLD) synthesis with rapid characterization techniques (X-Ray Diffraction, Atomic Force Microscopy, Electrochemical Impedance Spectroscopy, etc.) to quickly optimize metal oxide materials for energy conversion devices. The solid oxide fuel cell (SOFC) is one of the most promising energy conversion technologies. Despite years of concerted efforts by the research community, widespread commercialization of SOFCs is hindered by their high operating temperature requirements (>800 °C). Currently, there are limitations on the performance of electrolyte and cathode materials, which prevent a significant reduction in this operating temperature. To this end, we developed all-thin-film SOFC structures to probe fundamental transport properties via out-of-plane measurements in epitaxial electrolyte films with idealized interfaces. A highly conducting and thermally stable bottom electrode is combined with a library of top microelectrodes (30 ≤ ≤500), in a Cox and Strack-like geometry, which enables a direct and highspatial- resolution investigation of the intrinsic transport properties of the model electrolyte Sm0.2Ce0.8O2-δ (SDC20). This work demonstrated the utility of prototypical out-of-plane all-thin-film heteroepitaxial electrochemical devices as a model platform which can be extended to high-throughput investigations. We have used the high-throughput thin film formalism to develop a fundamental understanding of surface oxygen reduction reaction (ORR) mechanisms in mixed-conducting cathode materials by fabricating thin-film microelectrode arrays of La0.6Sr0.4Co1-xFexO3-δ (0≤x≤1) on a YSZ (100) substrate. The electrochemical properties of these microelectrode stacks are investigated via scanning impedance spectroscopy, and reveal that electrochemical resistance is dominated by surface oxygen exchange reactions on the electrode through a two-phase boundary pathway. A monotonic increase in electrochemical resistance is observed in La0.6Sr0.4Co1-xFexO3-δ from x =0 to x =1 along with a decrease in chemical capacitance corresponding to a decrease in oxygen vacancy concentration. A ( dependence of * and , for the whole spread film with the in a range of 0.5 to 0.75 is observed, indicating that the oxygen vacancy transport to surface-adsorbed oxygen intermediates is the ratedetermining step for mixed conducting cathodes. This study demonstrates the rich insights obtained via high-throughput methodologies and the promise of applying such techniques to discover highly active solid-state cathode materials. We have also looked at PrBa0.5Sr0.5Co1.5Fe0.5O5+δ (PBSCF) as a doubleperovskite cathode material, which exhibits the combined conduction of e-, O2-, and H+. The high capacity of PBSCF to adsorb H2O at high temperature (Proton concentration: 1.7 mol% at 600 °C) and its excellent ORR performance can facilitate the cathodic electrochemical reaction in proton conducting SOFCs (p-SOFCs). A thinfilm library was used to investigate the ORR mechanism for PBSCF by systematically varying the size of the microelectrode arrays. By combining a chemically stable electrolyte, BaZr0.4Ce0.4Y0.1Yb0.1O3 (BZCYYb4411) with a thin dense PLD PBSCF interface layer between the cathode material and the electrolyte, we have demonstrated breakthrough performance in p-SOFCs with a peak power density of 548 mW/cm2 at 500 °C and an unprecedented stability under CO2. The behavior of this p-SOFC can compete with that of high performance oxide-ion-conducting SOFCs. Such performance can create new avenues for incorporating fuel cells into a sustainable energy future. We have further developed a high-throughput pulsed laser deposition approach to grow phase pure and high quality crystalline V1-xWxO2 (0 ≤ x < 4%) thin films on different substrates, which is challenging because of the complex phase diagram of vanadium oxides where there are many polymorphs of VO2. We systematically study how tungsten doping affects the poorly-understood phase transition hysteresis via a composition-spread approach. We have demonstrated for the first time that a composition of V1-xWxO2 (x ≈ 2.4%) satisfies unique ‘cofactor conditions’ based on geometric nonlinear theory. Our findings inform a strategy for developing more reliable vanadium dioxide materials. In addition, the potential application of V1-xWxO2 thin films in lithium-ion rechargeable batteries were systematically studied based on the tungsten concentration dependence of electrical properties of V1-xWxO2

Observation of the superconducting proximity effect in the surface state of SmB6 thin films

The proximity effect at the interface between a topological insulator (TI) and a superconductor is predicted to give rise to chiral topological superconductivity and Majorana fermion excitations. In most TIs studied to date, however, the conducting bulk states have overwhelmed the transport properties and precluded the investigation of the interplay of the topological surface state and Cooper pairs. Here, we demonstrate the superconducting proximity effect in the surface state of SmB6 thin films which display bulk insulation at low temperatures. The Fermi velocity in the surface state deduced from the proximity effect is found to be as large as 10^5 m/s, in good agreement with the value obtained from a separate transport measurement. We show that high transparency between the TI and a superconductor is crucial for the proximity effect. The finding here opens the door to investigation of exotic quantum phenomena using all-thin-film multilayers with high-transparency interfaces

Synthesizing Physically Plausible Human Motions in 3D Scenes

Synthesizing physically plausible human motions in 3D scenes is a challenging problem. Kinematics-based methods cannot avoid inherent artifacts (e.g., penetration and foot skating) due to the lack of physical constraints. Meanwhile, existing physics-based methods cannot generalize to multi-object scenarios since the policy trained with reinforcement learning has limited modeling capacity. In this work, we present a framework that enables physically simulated characters to perform long-term interaction tasks in diverse, cluttered, and unseen scenes. The key idea is to decompose human-scene interactions into two fundamental processes, Interacting and Navigating, which motivates us to construct two reusable Controller, i.e., InterCon and NavCon. Specifically, InterCon contains two complementary policies that enable characters to enter and leave the interacting state (e.g., sitting on a chair and getting up). To generate interaction with objects at different places, we further design NavCon, a trajectory following policy, to keep characters' locomotion in the free space of 3D scenes. Benefiting from the divide and conquer strategy, we can train the policies in simple environments and generalize to complex multi-object scenes. Experimental results demonstrate that our framework can synthesize physically plausible long-term human motions in complex 3D scenes. Code will be publicly released at https://github.com/liangpan99/InterScene

Exceptional power density and stability at intermediate temperatures in protonic ceramic fuel cells

Over the past several years, important strides have been made in demonstrating protonic ceramic fuel cells (PCFCs). Such fuel cells offer the potential of environmentally sustainable and cost-effective electric power generation. However, their power outputs have lagged behind predictions based on their high electrolyte conductivities. Here we overcome PCFC performance and stability challenges by employing a high-activity cathode, PrBa_(0.5)Sr_(0.5)Co_(1.5)Fe_(0.5)O_(5+δ) (PBSCF), in combination with a chemically stable electrolyte, BaZr_(0.4)Ce_(0.4)Y_(0.1)Yb_(0.1)O_3 (BZCYYb4411). We deposit a thin dense interlayer film of the cathode material onto the electrolyte surface to mitigate contact resistance, an approach which is made possible by the proton permeability of PBSCF. The peak power densities of the resulting fuel cells exceed 500 mW cm^(−2) at 500 °C, while also offering exceptional, long-term stability under CO_2

Exceptional power density and stability at intermediate temperatures in protonic ceramic fuel cells

Over the past several years, important strides have been made in demonstrating protonic ceramic fuel cells (PCFCs). Such fuel cells offer the potential of environmentally sustainable and cost-effective electric power generation. However, their power outputs have lagged behind predictions based on their high electrolyte conductivities. Here we overcome PCFC performance and stability challenges by employing a high-activity cathode, PrBa_(0.5)Sr_(0.5)Co_(1.5)Fe_(0.5)O_(5+δ) (PBSCF), in combination with a chemically stable electrolyte, BaZr_(0.4)Ce_(0.4)Y_(0.1)Yb_(0.1)O_3 (BZCYYb4411). We deposit a thin dense interlayer film of the cathode material onto the electrolyte surface to mitigate contact resistance, an approach which is made possible by the proton permeability of PBSCF. The peak power densities of the resulting fuel cells exceed 500 mW cm^(−2) at 500 °C, while also offering exceptional, long-term stability under CO_2

Safety and efficacy of lentinan nasal drops in patients infected with the variant of COVID-19: a randomized, placebo-controlled trial

Objective: Lentinan has antiviral, anti-tumor, immunomodulatory, stimulating interferon production, and other pharmacological effects. Previous animal experiments have shown that lentinan nasal drops can assist [Corona Virus Disease 2019) COVID-19] vaccine to induce high levels of neutralizing antibodies and can effectively resist the invasion of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This study aimed to evaluate the safety and efficacy of lentinan nasal drops in patients infected with Omicron (SARS-CoV-2 variant) through a dose-escalation study and a placebo-controlled trial.Methods: A randomized, placebo-controlled trial. The study was divided into two phases: Phase I: a dose escalation trial in which 24 COVID-19 patients were enrolled, that is, 12 in the escalation dose group (50, 75, and 100 µg/day) and 12 in the standard treatment group. The aim was to evaluate the safety and tolerance of lentinan nasal drops. The second stage was a placebo-controlled study. The optimal dose group of the first stage was used as the therapeutic dose, and the sample size was expanded to verify the anti-COVID-19 efficacy of lentinan nasal drops.Results: In the dose-increasing study, lentinan nasal drops showed good safety, and no serious adverse reactions occurred. The virus shedding time of the 100 µg dose group was significantly shorter than that in the control group (7.75 ± 1.71 VS 13.41 ± 3.8 days) (p = 0.01), and the 100 µg/day lentinan nasal drops were tolerated well. The results of the placebo-controlled study showed that compared with that in the placebo group, the time for COVID-19 antigen to turn negative was significantly shorter in the 100 µg lentinan nasal drop group (p = 0.0298), but no significant difference was observed in symptom improvement between the two groups. In the placebo-controlled study, two patients experienced mild nasal discomfort with nasal drops, but the symptoms relieved themselves.Conclusion: Lentinan nasal drops are tolerated well and can shorten the time of virus clearance

Dynamic Scene Path Planning of UAVs Based on Deep Reinforcement Learning

Traditional unmanned aerial vehicle path planning methods focus on addressing planning issues in static scenes, struggle to balance optimality and real-time performance, and are prone to local optima. In this paper, we propose an improved deep reinforcement learning approach for UAV path planning in dynamic scenarios. Firstly, we establish a task scenario including an obstacle assessment model and model the UAV’s path planning problem using the Markov Decision Process. We translate the MDP model into the framework of reinforcement learning and design the state space, action space, and reward function while incorporating heuristic rules into the action exploration policy. Secondly, we utilize the Q function approximation of an enhanced D3QN with a prioritized experience replay mechanism and design the algorithm’s network structure based on the TensorFlow framework. Through extensive training, we obtain reinforcement learning path planning policies for both static and dynamic scenes and innovatively employ a visualized action field to analyze their planning effectiveness. Simulations demonstrate that the proposed algorithm can accomplish UAV dynamic scene path planning tasks and outperforms classical methods such as A*, RRT, and DQN in terms of planning effectiveness

Minimum-Effort Waypoint-Following Differential Geometric Guidance Law Design for Endo-Atmospheric Flight Vehicles

To improve the autonomous flight capability of endo-atmospheric flight vehicles, such as cruise missiles, drones, and other small, low-cost unmanned aerial vehicles (UAVs), a novel minimum-effort waypoint-following differential geometric guidance law (MEWFDGGL) is proposed in this paper. Using the classical differential geometry curve theory, the optimal guidance problem of endo-atmospheric flight vehicles is transformed into an optimal space curve design problem, where the guidance command is the curvature. On the one hand, the change in speed of the flight vehicle is decoupled from the guidance problem. In this way, the widely adopted constant speed hypothesis in the process of designing the guidance law is eliminated, and, hence, the performance of the proposed MEWFDGGL is not influenced by the varying speed of the flight vehicle. On the other hand, considering the onboard computational burden, a suboptimal form of the MEWFDGGL is proposed to solve the problem, where both the complexity and the computational burden of the guidance law dramatically increase as the number of waypoints increases. The theoretical analysis demonstrates that both the original MEWFDGGL and its suboptimal form can be applied to general waypoint-following tasks with an arbitrary number of waypoints. Finally, the superiority and effectiveness of the proposed MEWFDGGL are verified by a numerical simulation and flight experiments