Low initial Coulombic efficiency (ICE) is an important impediment to practical application of Li-rich layered oxides (LLOs), which is due to the irreversible oxygen release. It is generally considered that surface oxy...Low initial Coulombic efficiency (ICE) is an important impediment to practical application of Li-rich layered oxides (LLOs), which is due to the irreversible oxygen release. It is generally considered that surface oxygen vacancies are conducive to the improvement of ICE of LLOs. To reveal the relation of oxygen vacancies and ICE, sample PLO (Li-Mn-Cr-O) and its treated product (TLO) are comprehensive investigated in this work. During the treated process, part of oxygen atoms return to original constructed vacancies. It makes oxygen vacancies in sample TLO much poorer than those in sample PLO, and induces the formation of Li-poor spinel-layered integrated structure. Electrochemical measurement indicates the ICE of sample PLO is only 80.8%, while sample TLO is almost full reversible with the ICE of ~97.1%. In term of high-energy X-ray diffraction, scanning transmission electron microscopy, X-ray photoelectron spectroscopy and synchrotron hard/soft X-ray absorption spectroscopy, we discover that the ICE is difficult to be improved significantly just by building oxygen vacancies. LLOs with high ICE not only have to construct suitable oxygen vacancies, but also require other components with Li-poor structure to stabilize oxygen. This work provides deep insight into the mechanism of high ICE, and will contribute to the design and development of LLOs for next-generation high-energy lithium-ion batteries.展开更多
Bacterial genome sequencing is a powerful technique for studying the genetic diversity and evolution ofmicrobial populations.However,the detection of genomic variants from sequencing data is challenging due to the pre...Bacterial genome sequencing is a powerful technique for studying the genetic diversity and evolution ofmicrobial populations.However,the detection of genomic variants from sequencing data is challenging due to the presence of contamination,sequencing errors and multiple strains within the same species.Several bioinformatics tools have been developed to address these issues,but their performance and accuracy have not been systematically evaluated.In this study,we compared 10 variant detection pipelines using 18 simulated and 17 real datasets of high-throughput sequences froma bundle of representative bacteria.We assessed the sensitivity of each pipeline under different conditions of coverage,simulation and strain diversity.We also demonstrated the application of these tools to identify consistentmutations in a 30-time repeated sequencing dataset of Staphylococcus hominis.We found that HaplotypeCaller,but not Mutect2,from the GATK tool set showed the best performance in terms of accuracy and robustness.CFSAN and Snippy performed not as well in several simulated and real sequencing datasets.Our results provided a comprehensive benchmark and guidance for choosing the optimal variant detection pipeline for high-throughput bacterial genome sequencing data.展开更多
AutoDock Vina(Vina)is a widely adopted molecular docking tool,often regarded as a standard or used as a baseline in numerous studies.However,its computational process is highly time-consuming.The pioneering field-prog...AutoDock Vina(Vina)is a widely adopted molecular docking tool,often regarded as a standard or used as a baseline in numerous studies.However,its computational process is highly time-consuming.The pioneering field-programmable gate array(FPGA)-based accelerator of Vina,known as Vina-FPGA,offers a high energy-efficiency approach to speed up the docking process.However,the computation modules in the Vina-FPGA design are not efficiently used.This is due to Vina exhibiting irregular behaviors in the form of nested loops with changing upper bounds and differing control flows.Fortunately,Vina employs the Monte Carlo iterative search method,which requires independent computations for different random initial inputs.This characteristic provides an opportunity to implement further parallel computation designs.To this end,this paper proposes Vina-FPGA2,an inter-module pipeline design for further accelerating Vina-FPGA.First,we use individual computational task(Task)independence by sequentially filling Tasks into computation modules.Then,we implement an inter-module pipeline parallel design by the Tag Checker module and architectural modifications,named Vina-FPGA2-Baseline.Next,to achieve resource-efficient hardware implementation,we describe it as an optimization problem and develop a reinforcement learning-based solver.Targeting the Xilinx UltraScale XCKU060 platform,this solver yields a more efficient implementation,named Vina-FPGA2-Enhanced.Finally,experiments show that Vina-FPGA2-Enhanced achieves an average 12.6×performance improvement over the central processing unit(CPU)and a 3.3×improvement over Vina-FPGA.Compared to Vina-GPU,Vina-FPGA2 achieves a 7.2×enhancement in energy efficiency.展开更多
基金We thank the funding supports of the National Natural Science Foundation of China(Project Nos.51874104 and 52004070)the Key Technology and Supporting Platform of Genetic Engineering of Materials under States Key Project of Research and Development Plan of China(Project No.2016YFB0700600).The authors thank Cheng-Hao Chuang for the assistant with X-ray spectroscopy measurement.
文摘Low initial Coulombic efficiency (ICE) is an important impediment to practical application of Li-rich layered oxides (LLOs), which is due to the irreversible oxygen release. It is generally considered that surface oxygen vacancies are conducive to the improvement of ICE of LLOs. To reveal the relation of oxygen vacancies and ICE, sample PLO (Li-Mn-Cr-O) and its treated product (TLO) are comprehensive investigated in this work. During the treated process, part of oxygen atoms return to original constructed vacancies. It makes oxygen vacancies in sample TLO much poorer than those in sample PLO, and induces the formation of Li-poor spinel-layered integrated structure. Electrochemical measurement indicates the ICE of sample PLO is only 80.8%, while sample TLO is almost full reversible with the ICE of ~97.1%. In term of high-energy X-ray diffraction, scanning transmission electron microscopy, X-ray photoelectron spectroscopy and synchrotron hard/soft X-ray absorption spectroscopy, we discover that the ICE is difficult to be improved significantly just by building oxygen vacancies. LLOs with high ICE not only have to construct suitable oxygen vacancies, but also require other components with Li-poor structure to stabilize oxygen. This work provides deep insight into the mechanism of high ICE, and will contribute to the design and development of LLOs for next-generation high-energy lithium-ion batteries.
基金supported by Zhejiang Provincial Natural Science Foundation(LY20H030006)Key Research&Development Program of Zhejiang(2023C03045)+2 种基金Fundamental Research Funds for the Central Universities(2022ZFJH003)Jinan Microecological Biomedicine Shandong Laboratory(JNL-2022036C)Public Welfare Project of Jinhua City,Zhejiang(2021-4-359).
文摘Bacterial genome sequencing is a powerful technique for studying the genetic diversity and evolution ofmicrobial populations.However,the detection of genomic variants from sequencing data is challenging due to the presence of contamination,sequencing errors and multiple strains within the same species.Several bioinformatics tools have been developed to address these issues,but their performance and accuracy have not been systematically evaluated.In this study,we compared 10 variant detection pipelines using 18 simulated and 17 real datasets of high-throughput sequences froma bundle of representative bacteria.We assessed the sensitivity of each pipeline under different conditions of coverage,simulation and strain diversity.We also demonstrated the application of these tools to identify consistentmutations in a 30-time repeated sequencing dataset of Staphylococcus hominis.We found that HaplotypeCaller,but not Mutect2,from the GATK tool set showed the best performance in terms of accuracy and robustness.CFSAN and Snippy performed not as well in several simulated and real sequencing datasets.Our results provided a comprehensive benchmark and guidance for choosing the optimal variant detection pipeline for high-throughput bacterial genome sequencing data.
基金Project supported by the National Natural Science Foundation of China(No.92464301)the Big Data Computing Center of Southeast University。
文摘AutoDock Vina(Vina)is a widely adopted molecular docking tool,often regarded as a standard or used as a baseline in numerous studies.However,its computational process is highly time-consuming.The pioneering field-programmable gate array(FPGA)-based accelerator of Vina,known as Vina-FPGA,offers a high energy-efficiency approach to speed up the docking process.However,the computation modules in the Vina-FPGA design are not efficiently used.This is due to Vina exhibiting irregular behaviors in the form of nested loops with changing upper bounds and differing control flows.Fortunately,Vina employs the Monte Carlo iterative search method,which requires independent computations for different random initial inputs.This characteristic provides an opportunity to implement further parallel computation designs.To this end,this paper proposes Vina-FPGA2,an inter-module pipeline design for further accelerating Vina-FPGA.First,we use individual computational task(Task)independence by sequentially filling Tasks into computation modules.Then,we implement an inter-module pipeline parallel design by the Tag Checker module and architectural modifications,named Vina-FPGA2-Baseline.Next,to achieve resource-efficient hardware implementation,we describe it as an optimization problem and develop a reinforcement learning-based solver.Targeting the Xilinx UltraScale XCKU060 platform,this solver yields a more efficient implementation,named Vina-FPGA2-Enhanced.Finally,experiments show that Vina-FPGA2-Enhanced achieves an average 12.6×performance improvement over the central processing unit(CPU)and a 3.3×improvement over Vina-FPGA.Compared to Vina-GPU,Vina-FPGA2 achieves a 7.2×enhancement in energy efficiency.
