We present the development of a versatile apparatus for 6.2 eV laser-based time and angle-resolved photoemission spectroscopy with micrometer spatial resolution (time-resolved μ-ARPES). With a combination of tunable spatial resolution down to ∼11 μm, high energy resolution (∼11 meV), near-transform-limited temporal resolution (∼280 fs), and tunable 1.55 eV pump fluence up to 3 mJ/cm2, this time-resolved μ-ARPES system enables the measurement of ultrafast electron dynamics in exfoliated and inhomogeneous materials. We demonstrate the performance of our system by correlating the spectral broadening of the topological surface state of Bi2Se3 with the spatial dimension of the probe pulse, as well as resolving the spatial inhomogeneity contribution to the observed spectral broadening. Finally, after in situ exfoliation, we performed time-resolved μ-ARPES on a ∼30 μm flake of transition metal dichalcogenide WTe2, thus demonstrating the ability to access ultrafast electron dynamics with momentum resolution on micro-exfoliated materials.
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http://dx.doi.org/10.1063/5.0176170 | DOI Listing |
Plants (Basel)
January 2025
Laboratory of Precision Agriculture (LAP), Department of Biosystems Engineering, "Luiz de Queiroz" College of Agriculture (ESALQ), University of São Paulo (USP), Piracicaba 13418-900, São Paulo, Brazil.
Coffee yield exhibits plant-level variability; however, due to operational issues, especially in smaller operations, the scouting and management of coffee yields are often hindered. Thus, a cell-size approach at the field level is proposed as a simple and efficient solution to overcome these constraints. This study aimed to present the feasibility of a cell-size approach to characterize spatio-temporal coffee production based on soil and plant attributes and yield (biennial effects) and to assess strategies for enhanced soil fertilization recommendations and economic results.
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January 2025
Department of Biomedicine, Texas A&M University, College Station, TX 77843, USA.
Recent developments in single-cell multi-omics technologies have provided the ability to identify diverse cell types and decipher key components of the tumor microenvironment (TME), leading to important advancements toward a much deeper understanding of how tumor microenvironment heterogeneity contributes to cancer progression and therapeutic resistance. These technologies are able to integrate data from molecular genomic, transcriptomic, proteomics, and metabolomics studies of cells at a single-cell resolution scale that give rise to the full cellular and molecular complexity in the TME. Understanding the complex and sometimes reciprocal relationships among cancer cells, CAFs, immune cells, and ECs has led to novel insights into their immense heterogeneity in functions, which can have important consequences on tumor behavior.
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January 2025
Faculty of Architecture and Civil Engineering, Karlsruhe University of Applied Sciences, 76133 Karlsruhe, Germany.
Engineers, geomorphologists, and ecologists acknowledge the need for temporally and spatially resolved measurements of sediment clogging (also known as colmation) in permeable gravel-bed rivers due to its adverse impacts on water and habitat quality. In this paper, we present a novel method for non-destructive, real-time measurements of pore-scale sediment deposition and monitoring of clogging by using wire-mesh sensors (WMSs) embedded in spheres, forming a smart gravel bed (GravelSens). The measuring principle is based on one-by-one voltage excitation of transmitter electrodes, followed by simultaneous measurements of the resulting current by receiver electrodes at each crossing measuring pores.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Mechnical and Vehicle Engineering, Hunan University, Changsha 411082, China.
Chip defect detection is a crucial aspect of the semiconductor production industry, given its significant impact on chip performance. This paper proposes a lightweight neural network with dual decoding paths for LED chip segmentation, named LDDP-Net. Within the LDDP-Net framework, the receptive field of the MobileNetv3 backbone is modified to mitigate information loss.
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January 2025
College of Geology Engineering and Geomatics, Chang'an University, Xi'an 710054, China.
Precipitable water vapor (PWV) is an important indicator to characterize the spatial and temporal variability of water vapor. A high spatial and temporal resolution of atmospheric precipitable water can be obtained using ground-based GNSS, but its inversion accuracy is usually limited by the weighted mean temperature, Tm. For this reason, based on the data of 17 ground-based GNSS stations and water vapor reanalysis products over 2 years in the Hong Kong region, a new model for water vapor inversion without the Tm parameter is established by deep learning in this paper, the research results showed that, compared with the PWV information calculated by the traditional model using Tm parameter, the accuracy of the PWV retrieved by the new model proposed in this paper is higher, and its accuracy index parameters BIAS, MAE, and RMSE are improved by 38% on average.
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