Driving force induced transition in thermal behavior of grain boundary migration in Ni

Grain boundaries (GBs) that show higher mobility at lower temperatures (i.e., anti-thermal or non-Arrhenius behavior) have attracted significant interest in recent years. In this study, we use atomistic simulations to systematically investigate the effect of driving force on GB mobility based on a set of bicrystalline models in Ni. It is found that the thermal behavior of GB migration strongly depends on temperature and the magnitude of driving forces. When the driving force is at the zero-driving force limit as induced solely by thermal fluctuations, the mobility of all GBs investigated in the current study shows a transition from thermally activated to anti-thermal behavior when the temperature is increased. As the driving force increases, the transition temperature at which the mobility peaks would gradually decrease so that for some GBs only the anti-thermal behavior would be detected. Energy analysis further reveals that the transition temperature (Ttrans) is linearly related to both energy barrier per area (E) from NEB simulation and the fitted apparent activation (Q) energy, and both E and Q are lowered as the driving force increases. Our work supports the previous theoretical models for GB migration based on both classical thermal activation and disconnection nucleation. Furthermore, the current study can be used to improve both models by considering the influence of driving force with a simple fix to how the energy barrier for GB migration should be considered. It is expected that this work advances the current understanding of general GB migration and sheds some light on a unified theoretical framework in the near future

Fine structure characterization of zero-valent iron nanoparticles for decontamination of nitrites and nitrates in wastewater and groundwater

The main objectives of the present study were to investigate the chemical reduction of nitrate or nitrite species by zero-valent iron nanoparticle (ZVIN) in aqueous solution and related reaction kinetics or mechanisms using fine structure characterization. This work also exemplifies the utilization of field emission-scanning electron microscope (FE-SEM), transmission electron microscopy (TEM), and x-ray diffraction (XRD) to reveal the speciation and possible reaction pathway in a very complex adsorption and redox reaction process. Experimentally, ZVIN of this study was prepared by sodium borohydride reduction method at room temperature and ambient pressure. The morphology of as-synthesized ZVIN shows that the nearly ball and ultrafine particles ranged of 20-50 nm were observed with FE-SEM or TEM analysis. The kinetic model of nitrites or nitrates reductive reaction by ZVIN is proposed as a pseudo first-order kinetic equation. The nitrite and nitrate removal efficiencies using ZVIN were found 65-83% and 51-68%, respectively, based on three different initial concentrations. Based on the XRD pattern analyses, it is found that the quantitative relationship between nitrite and Fe(III) or Fe(II) is similar to the one between nitrate and Fe( III) in the ZVIN study. The possible reason is due to the faster nitrite reduction by ZVIN. In fact, the occurrence of the relative faster nitrite reductive reaction suggested that the passivation of the ZVIN have a significant contribution to iron corrosion. The extended x-ray absorption fine structure (EXAFS) or x-ray absorption near edge structure (XANES) spectra show that the nitrites or nitrates reduce to N-2 or NH3 while oxidizing the ZVIN to Fe2O3 or Fe3O4 electrochemically. It is also very clear that decontamination of nitrate or nitrite species in groundwater via the in-situ remediation with a ZVIN permeable reactive barrier would be environmentally attractive

Unusual acceleration and size effects in grain boundary migration with shear coupling

Grain boundary (GB) migration plays a crucial role in the thermal and mechanical responses of polycrystalline materials, particularly in ultrafine-grained and nano-grained materials exhibiting grain size-dependent properties. This study investigates the migration behaviors of a set of GBs in Ni through atomistic simulations, employing synthetic driving forces and shear stress. Surprisingly, the displacements of some shear-coupling GBs do not follow the widely assumed linear or approximately linear relation with time; instead, they exhibit a noticeable acceleration tendency. Furthermore, as the bicrystal size perpendicular to the GB plane increases, the boundary velocity significantly decreases. These observations are independent of the magnitude and type of driving force but are closely linked to temperature, unique to shear-coupling GBs that display a rise in the kinetic energy component along the shear direction. By adopting a specific boundary condition, the acceleration in migration and size effect can be largely alleviated. However, the continuous rise in kinetic energy persists, leading to the true driving force for GB migration being lower than the applied value. To address this, we propose a technique to extract the true driving force based on a quantitative analysis of the work-energy relation in the bicrystal system. The calculated true mobility reveals that the recently proposed mobility tensor may not be symmetric at relatively large driving forces. These discoveries advance our understanding of GB migration and offer a scheme to extract the true mobility, crucial for meso- and continuum-scale simulations of GB migration-related phenomena such as crack propagation, recrystallization, and grain growth.Comment: 28 pages, 10 Figure

Realization of generalized quantum searching using nuclear magnetic resonance

According to the theoretical results, the quantum searching algorithm can be generalized by replacing the Walsh-Hadamard(W-H) transform by almost any quantum mechanical operation. We have implemented the generalized algorithm using nuclear magnetic resonance techniques with a solution of chloroform molecules. Experimental results show the good agreement between theory and experiment.Comment: 11 pages,3 figure. Accepted by Phys. Rev. A. Scheduled Issue: 01 Mar 200

Incorporating External Knowledge and Goal Guidance for LLM-based Conversational Recommender Systems

This paper aims to efficiently enable large language models (LLMs) to use external knowledge and goal guidance in conversational recommender system (CRS) tasks. Advanced LLMs (e.g., ChatGPT) are limited in domain-specific CRS tasks for 1) generating grounded responses with recommendation-oriented knowledge, or 2) proactively leading the conversations through different dialogue goals. In this work, we first analyze those limitations through a comprehensive evaluation, showing the necessity of external knowledge and goal guidance which contribute significantly to the recommendation accuracy and language quality. In light of this finding, we propose a novel ChatCRS framework to decompose the complex CRS task into several sub-tasks through the implementation of 1) a knowledge retrieval agent using a tool-augmented approach to reason over external Knowledge Bases and 2) a goal-planning agent for dialogue goal prediction. Experimental results on two multi-goal CRS datasets reveal that ChatCRS sets new state-of-the-art benchmarks, improving language quality of informativeness by 17% and proactivity by 27%, and achieving a tenfold enhancement in recommendation accuracy.Comment: Main paper 8 pages; References and Appendix 9 pages; 7 figures and 14 table

Location-aware Graph Convolutional Networks for Video Question Answering

We addressed the challenging task of video question answering, which requires machines to answer questions about videos in a natural language form. Previous state-of-the-art methods attempt to apply spatio-temporal attention mechanism on video frame features without explicitly modeling the location and relations among object interaction occurred in videos. However, the relations between object interaction and their location information are very critical for both action recognition and question reasoning. In this work, we propose to represent the contents in the video as a location-aware graph by incorporating the location information of an object into the graph construction. Here, each node is associated with an object represented by its appearance and location features. Based on the constructed graph, we propose to use graph convolution to infer both the category and temporal locations of an action. As the graph is built on objects, our method is able to focus on the foreground action contents for better video question answering. Lastly, we leverage an attention mechanism to combine the output of graph convolution and encoded question features for final answer reasoning. Extensive experiments demonstrate the effectiveness of the proposed methods. Specifically, our method significantly outperforms state-of-the-art methods on TGIF-QA, Youtube2Text-QA, and MSVD-QA datasets. Code and pre-trained models are publicly available at: https://github.com/SunDoge/L-GC

Generating Visually Aligned Sound from Videos

We focus on the task of generating sound from natural videos, and the sound should be both temporally and content-wise aligned with visual signals. This task is extremely challenging because some sounds generated \emph{outside} a camera can not be inferred from video content. The model may be forced to learn an incorrect mapping between visual content and these irrelevant sounds. To address this challenge, we propose a framework named REGNET. In this framework, we first extract appearance and motion features from video frames to better distinguish the object that emits sound from complex background information. We then introduce an innovative audio forwarding regularizer that directly considers the real sound as input and outputs bottlenecked sound features. Using both visual and bottlenecked sound features for sound prediction during training provides stronger supervision for the sound prediction. The audio forwarding regularizer can control the irrelevant sound component and thus prevent the model from learning an incorrect mapping between video frames and sound emitted by the object that is out of the screen. During testing, the audio forwarding regularizer is removed to ensure that REGNET can produce purely aligned sound only from visual features. Extensive evaluations based on Amazon Mechanical Turk demonstrate that our method significantly improves both temporal and content-wise alignment. Remarkably, our generated sound can fool the human with a 68.12% success rate. Code and pre-trained models are publicly available at https://github.com/PeihaoChen/regnetComment: Published in IEEE Transactions on Image Processing, 2020. Code, pre-trained models and demo video: https://github.com/PeihaoChen/regne