In Situ Tem Study on the Nanomechanical Behaviors of Metallic Nanowires

In this dissertation, the relationships between structure-mechanical properties-deformation mechanisms in face-centered cubic (FCC) and body-centered cubic (BCC) metallic nanowires have been studied using in situ TEM nanomechanical testing. It is well known that smaller is stronger and the widely-observed size effect is believed to arise from dislocation interactions inside nanocrystals. However, the interaction mechanisms in small-volume nanocrystals remain unexplored. Here, it was found that surface-nucleated dislocations can strongly interact inside the confined volume of Au nanowires, which led to a new type of dislocation-originated stacking fault tetrahedra (SFT), at variance to the widely-believed vacancy-originated SFT. The atomic-scale processes of nucleation, migration and annihilation of dislocation-originated SFT were revealed, shedding new light onto the strain hardening and size effect in small-volume materials. Although nanoscale twinning is an effective mean to enhance the strength of metals, twin-size effect on the deformation and failure of nanotwinned metals remains largely unexplored, especially at the minimum twin size. Here, a new type of size effect in nanotwinned Au nanowires is presented. As twin size reaches the angstrom-scale, Au nanowires exhibit a remarkable ductile-to-brittle transition that is governed by the heterogeneous-to-homogeneous dislocation nucleation transition. Quantitative measurements show that approaching such a nanotwin size limit gives rise to the ultra-high strength in Au nanowires, close to the ideal strength limit of perfect Au. Twinning is a fundamental deformation mode that competes against dislocation slip in crystals. At the nanoscale, higher stresses are required for plastic deformation than that in bulk, which favors twinning over dislocation slip. Indeed, deformation twinning has been well-documented in FCC nanocrystals. However, it remains unexplored in BCC nanostructures. Here, it shows that twinning is the dominant deformation mode in BCC-tungsten nanocrystals. A competition between twinning and dislocation slip occurs when changing the loading orientations, attributed to the defect growth controlled plasticity in BCC nanocrystals. Several important commonalities and differences in FCC and BCC nanocrystals deforming at nanoscale are further proposed. The results from this dissertation advance fundamental understanding of plastic deformation in a broad class of metals and alloys, and are of technological importance for degradation control and future design of ultra-strength nanomaterials

Spatial-Temporal-Aware Safe Multi-Agent Reinforcement Learning of Connected Autonomous Vehicles in Challenging Scenarios

Communication technologies enable coordination among connected and autonomous vehicles (CAVs). However, it remains unclear how to utilize shared information to improve the safety and efficiency of the CAV system. In this work, we propose a framework of constrained multi-agent reinforcement learning (MARL) with a parallel safety shield for CAVs in challenging driving scenarios. The coordination mechanisms of the proposed MARL include information sharing and cooperative policy learning, with Graph Convolutional Network (GCN)-Transformer as a spatial-temporal encoder that enhances the agent's environment awareness. The safety shield module with Control Barrier Functions (CBF)-based safety checking protects the agents from taking unsafe actions. We design a constrained multi-agent advantage actor-critic (CMAA2C) algorithm to train safe and cooperative policies for CAVs. With the experiment deployed in the CARLA simulator, we verify the effectiveness of the safety checking, spatial-temporal encoder, and coordination mechanisms designed in our method by comparative experiments in several challenging scenarios with the defined hazard vehicles (HAZV). Results show that our proposed methodology significantly increases system safety and efficiency in challenging scenarios.Comment: This paper has been accepted by the 2023 IEEE International Conference on Robotics and Automation (ICRA 2023). 6 pages, 5 figure

Enhanced temperature sensing by multi-mode coupling in an on-chip microcavity system

The micro-cavity is a promising sensor platform, any perturbation would disturb its linewidth, cause resonance shift or splitting. However, such sensing resolution is limited by the cavity's optical quality factor and mode volume. Here we propose and demonstrate in an on-chip integrated microcavity system that resolution of a self referenced sensor could be enhanced with multi mode coupling

Revealing the pulse-induced electroplasticity by decoupling electron wind force

Micro/nano electromechanical systems and nanodevices often suffer from degradation under electrical pulse. However, the origin of pulse-induced degradation remains an open question. Herein, we investigate the defect dynamics in Au nanocrystals under pulse conditions. By decoupling the electron wind force via a properly-designed in situ TEM electropulsing experiment, we reveal a non-directional migration of Σ3{112} incoherent twin boundary upon electropulsing, in contrast to the expected directional migration under electron wind force. Quantitative analyses demonstrate that such exceptional incoherent twin boundary migration is governed by the electron-dislocation interaction that enhances the atom vibration at dislocation cores, rather than driven by the electron wind force in classic model. Our observations provide valuable insights into the origin of electroplasticity in metallic materials at the atomic level, which are of scientific and technological significances to understanding the electromigration and resultant electrical damage/failure inmicro/ nano-electronic devices

Shared Information-Based Safe And Efficient Behavior Planning For Connected Autonomous Vehicles

The recent advancements in wireless technology enable connected autonomous vehicles (CAVs) to gather data via vehicle-to-vehicle (V2V) communication, such as processed LIDAR and camera data from other vehicles. In this work, we design an integrated information sharing and safe multi-agent reinforcement learning (MARL) framework for CAVs, to take advantage of the extra information when making decisions to improve traffic efficiency and safety. We first use weight pruned convolutional neural networks (CNN) to process the raw image and point cloud LIDAR data locally at each autonomous vehicle, and share CNN-output data with neighboring CAVs. We then design a safe actor-critic algorithm that utilizes both a vehicle's local observation and the information received via V2V communication to explore an efficient behavior planning policy with safety guarantees. Using the CARLA simulator for experiments, we show that our approach improves the CAV system's efficiency in terms of average velocity and comfort under different CAV ratios and different traffic densities. We also show that our approach avoids the execution of unsafe actions and always maintains a safe distance from other vehicles. We construct an obstacle-at-corner scenario to show that the shared vision can help CAVs to observe obstacles earlier and take action to avoid traffic jams.Comment: This paper gets the Best Paper Award in the DCAA workshop of AAAI 202

Twinning-assisted dynamic adjustment of grain boundary mobility

Grain boundary (GB) plasticity dominates the mechanical behaviours of nanocrystalline materials. Under mechanical loading, GB configuration and its local deformation geometry change dynamically with the deformation; the dynamic variation of GB deformability, however, remains largely elusive, especially regarding its relation with the frequently-observed GB-associated deformation twins in nanocrystalline materials. Attention here is focused on the GB dynamics in metallic nanocrystals, by means of well-designed in situ nanomechanical testing integrated with molecular dynamics simulations. GBs with low mobility are found to dynamically adjust their configurations and local deformation geometries via crystallographic twinning, which instantly changes the GB dynamics and enhances the GB mobility. This selfadjust twin-assisted GB dynamics is found common in a wide range of face-centred cubic nanocrystalline metals under different deformation conditions. These findings enrich our understanding of GB-mediated plasticity, especially the dynamic behaviour of GBs, and bear practical implication for developing high performance nanocrystalline materials through interface engineering

Multi-Agent Reinforcement Learning Guided by Signal Temporal Logic Specifications

Reward design is a key component of deep reinforcement learning, yet some tasks and designer's objectives may be unnatural to define as a scalar cost function. Among the various techniques, formal methods integrated with DRL have garnered considerable attention due to their expressiveness and flexibility to define the reward and requirements for different states and actions of the agent. However, how to leverage Signal Temporal Logic (STL) to guide multi-agent reinforcement learning reward design remains unexplored. Complex interactions, heterogeneous goals and critical safety requirements in multi-agent systems make this problem even more challenging. In this paper, we propose a novel STL-guided multi-agent reinforcement learning framework. The STL requirements are designed to include both task specifications according to the objective of each agent and safety specifications, and the robustness values of the STL specifications are leveraged to generate rewards. We validate the advantages of our method through empirical studies. The experimental results demonstrate significant reward performance improvements compared to MARL without STL guidance, along with a remarkable increase in the overall safety rate of the multi-agent systems

Lack of association between polymorphisms of MASP2 and susceptibility to SARS coronavirus infection

<p>Abstract</p> <p>Background</p> <p>The pathogenesis of severe acute respiratory disease syndrome (SARS) is not fully understood. One case-control study has reported an association between susceptibility to SARS and <it>mannan-binding lectin </it>(<it>MBL</it>) in China. As the downstream protein of <it>MBL</it>, variants of the <it>MBL</it>-associated serine protease-2 (<it>MASP2</it>) gene may be associated with SARS coronavirus (SARS-CoV) infection in the same population.</p> <p>Methods</p> <p>Thirty individuals with SARS were chosen for analysis of <it>MASP2 </it>polymorphisms by means of PCR direct sequencing. Tag single nucleotide polymorphisms (tagSNPs) were chosen using pairwise tagging algorithms. The frequencies of four tag SNPs (rs12711521, rs2261695, rs2273346 and rs7548659) were ascertained in 376 SARS patients and 523 control subjects, using the Beckman SNPstream Ultra High Throughput genotyping platform.</p> <p>Results</p> <p>There is no significant association between alleles or genotypes of the <it>MASP2 </it>tagSNP and susceptibility to SARS-CoV in both Beijing and Guangzhou populations. Diplotype (rs2273346 and rs12711521)were analyzed for association with susceptibility to SARS, no statistically significant evidence of association was observed. The Beijing and Guangzhou sample groups were homogeneous regarding demographic and genetic parameters, a joined analysis also showed no statistically significant evidence of association.</p> <p>Conclusion</p> <p>Our data do not suggest a role for <it>MASP2 </it>polymorphisms in SARS susceptibility in northern and southern China.</p