34 research outputs found
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
A Polymeric Liquid Membrane Electrode Responsive to 3,3′,5,5′-Tetramethylbenzidine Oxidation for Sensitive Peroxidase/Peroxidase Mimetic-Based Potentiometric Biosensing
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