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487 results on '"Chen, Qingjun"'

1. On the electrochemical CO2 reduction by Bi-based catalysts: single crystals or mixture phases

Author

Zhou, Mengting, Liu, Hongxia, Yan, Juntao, Chen, Qingjun, Chen, Rong, and Liu, Lei

Subjects
Physics - Chemical Physics, Condensed Matter - Materials Science
Abstract

Metallic bismuth is both non-toxic and cost-effective. Bi-based catalysts have demonstrated the ability to efficiently produce HCOOH through CO2RR while effectively inhibiting the HER. Although many experiments have been reported concerning its performance towards CO2 reduction, the impact its valence states and crystal faces on CO2RR selectivity (e.g. HCOOH versus CO) it still under debate. Here, we performed a comprehensive study via density functional theory, by including three typical valence states of Bi, such as 0 (Bi), +3 (Bi2O3) and +5 (Bi2O5), as well as their often-studied crystal facets. The results show that metallic Bi demonstrates a poor selectivity for HCOOH, but boasts a higher conversion rate for CO2. While Bi2O3 exhibits a good selectivity for HCOOH production, yet it displays a lower conversion rate for CO2. For Bi2O5, all studied surfaces show high energy barriers in both cases of HCOOH and CO production, and lower energy barriers for HER reactions, indicating that Bi at +5 valence state is not the good choice for 2e transfer reactions. Subsequently, we further examined the effects of oxygen contents on the selectivity of HCOOH and the conversion rate for CO2. Interestingly, we found that partial oxidization of Bi benefits both the selectivity and the conversion rate. With these observations, we suggest that a mixture of Bi (0) and Bi2O3 (+3) phases would be a better choice than single crystals for future experiments.

Published
2024

2. TraceDiag: Adaptive, Interpretable, and Efficient Root Cause Analysis on Large-Scale Microservice Systems

Author

Ding, Ruomeng, Zhang, Chaoyun, Wang, Lu, Xu, Yong, Ma, Minghua, Wu, Xiaomin, Zhang, Meng, Chen, Qingjun, Gao, Xin, Gao, Xuedong, Fan, Hao, Rajmohan, Saravan, Lin, Qingwei, and Zhang, Dongmei

Subjects
Computer Science - Software Engineering
Abstract

Root Cause Analysis (RCA) is becoming increasingly crucial for ensuring the reliability of microservice systems. However, performing RCA on modern microservice systems can be challenging due to their large scale, as they usually comprise hundreds of components, leading significant human effort. This paper proposes TraceDiag, an end-to-end RCA framework that addresses the challenges for large-scale microservice systems. It leverages reinforcement learning to learn a pruning policy for the service dependency graph to automatically eliminates redundant components, thereby significantly improving the RCA efficiency. The learned pruning policy is interpretable and fully adaptive to new RCA instances. With the pruned graph, a causal-based method can be executed with high accuracy and efficiency. The proposed TraceDiag framework is evaluated on real data traces collected from the Microsoft Exchange system, and demonstrates superior performance compared to state-of-the-art RCA approaches. Notably, TraceDiag has been integrated as a critical component in the Microsoft M365 Exchange, resulting in a significant improvement in the system's reliability and a considerable reduction in the human effort required for RCA.

Published
2023

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