Exciton diffusion in semiconducting single-wall carbon nanotubes studied by transient absorption microscopy

Spatiotemporal dynamics of excitons in isolated semiconducting single-walled carbon nanotubes are studied using transient absorption microscopy. Differential reflection and transmission of an 810-nm probe pulse after excitation by a 750-nm pump pulse are measured. We observe a bi-exponentially decaying signal with a fast time constant of 0.66 ps and a slower time constant of 2.8 ps. Both constants are independent of the pump fluence. By spatially and temporally resolving the differential reflection, we are able to observe a diffusion of excitons, and measure a diffusion coefficient of 200 cm2/s at room temperature and 300 cm2/s at lower temperatures of 10 K and 150 K.Comment: 6 pages, 4 figure

Exciton diffusion in semiconducting single-walled carbon nanotubes studied by transient absorption microscopy

This is the publisher's version, also available electronically from http://journals.aps.org/prb/abstract/10.1103/PhysRevB.86.205417.Spatiotemporal dynamics of excitons in isolated semiconducting single-walled carbon nanotubes are studied using transient absorption microscopy. Differential reflection and transmission of an 810-nm probe pulse after excitation by a 750-nm pump pulse are measured. We observe a biexponentially decaying signal with a fast time constant of 0.66 ps and a slower time constant of 2.8 ps. Both constants are independent of the pump fluence. By spatially and temporally resolving the differential reflection, we are able to observe a diffusion of excitons, and measure a diffusion coefficient of 200±10 cm2/s at room temperature and 300±10 cm2/s at lower temperatures of 10 K and 150 K

X-ray emission for 424 MeV/u C ions impacting on selected targets

In inertial Confinement Fusion (ICF), X-ray radiation drives the implosion requiring not only sufficient conversion efficiency of the drive energy to the X-ray but also the highly spatial symmetry..

Demystifying Compiler Unstable Feature Usage and Impacts in the Rust Ecosystem

Rust programming language is gaining popularity rapidly in building reliable and secure systems due to its security guarantees and outstanding performance. To provide extra functionalities, the Rust compiler introduces Rust unstable features (RUF) to extend compiler functionality, syntax, and standard library support. However, these features are unstable and may get removed, introducing compilation failures to dependent packages. Even worse, their impacts propagate through transitive dependencies, causing large-scale failures in the whole ecosystem. Although RUF is widely used in Rust, previous research has primarily concentrated on Rust code safety, with the usage and impacts of RUF from the Rust compiler remaining unexplored. Therefore, we aim to bridge this gap by systematically analyzing the RUF usage and impacts in the Rust ecosystem. We propose novel techniques for extracting RUF precisely, and to assess its impact on the entire ecosystem quantitatively, we accurately resolve package dependencies. We have analyzed the whole Rust ecosystem with 590K package versions and 140M transitive dependencies. Our study shows that the Rust ecosystem uses 1000 different RUF, and at most 44% of package versions are affected by RUF, causing compiling failures for at most 12%. To mitigate wide RUF impacts, we further design and implement a RUF-compilation-failure recovery tool that can recover up to 90% of the failure. We believe our techniques, findings, and tools can help to stabilize the Rust compiler, ultimately enhancing the security and reliability of the Rust ecosystem.Comment: Published in ICSE'2024 Conference: https://conf.researchr.org/details/icse-2024/icse-2024-research-track/6/Demystifying-Compiler-Unstable-Feature-Usage-and-Impacts-in-the-Rust-Ecosystem. Project webiste: https://sites.google.com/view/ruf-study/home. Released Source Code Zonodo: https://zenodo.org/records/828937

Smart grid power load type forecasting: research on optimization methods of deep learning models

Introduction: In the field of power systems, power load type prediction is a crucial task. Different types of loads, such as domestic, industrial, commercial, etc., have different energy consumption patterns. Therefore, accurate prediction of load types can help the power system better plan power supply strategies to improve energy utilization and stability. However, this task faces multiple challenges, including the complex topology of the power system, the diversity of time series data, and the correlation between data. With the rapid development of deep learning methods, researchers are beginning to leverage these powerful techniques to address this challenge. This study aims to explore how to optimize deep learning models to improve the accuracy of load type prediction and provide support for efficient energy management and optimization of smart grids.Methods: In this study, we propose a deep learning method that combines graph convolutional networks (GCN) and sequence-to-sequence (Seq2Seq) models and introduces an attention mechanism. The methodology involves multiple steps: first, we use the GCN encoder to process the topological structure information of the power system and encode node features into a graph data representation. Next, the Seq2Seq decoder takes the historical time series data as the input sequence and generates a prediction sequence of the load type. We then introduced an attention mechanism, which allows the model to dynamically adjust its attention to input data and better capture the relationship between time series data and graph data.Results: We conducted extensive experimental validation on four different datasets, including the National Grid Electricity Load Dataset, the Canadian Electricity Load Dataset, the United States Electricity Load Dataset, and the International Electricity Load Dataset. Experimental results show that our method achieves significant improvements in load type prediction tasks. It exhibits higher accuracy and robustness compared to traditional methods and single deep learning models. Our approach demonstrates advantages in improving load type prediction accuracy, providing strong support for the future development of the power system.Discussion: The results of our study highlight the potential of deep learning techniques, specifically the combination of GCN and Seq2Seq models with attention mechanisms, in addressing the challenges of load type prediction in power systems. By improving prediction accuracy and robustness, our approach can contribute to more efficient energy management and the optimization of smart grids