Measuring polarisation, spectrum, temporal dynamics, and spatial complex amplitude of optical beams is essential to studying phenomena in laser dynamics, telecommunications and nonlinear optics. Current characterisation techniques apply in limited contexts. Non-interferometric methods struggle to distinguish spatial phase, while phase-sensitive approaches necessitate either an auxiliary reference source or a self-reference, neither of which is universally available. Deciphering complex wavefronts of multiple co-propagating incoherent fields remains particularly challenging. We harness principles of spatial state tomography to circumvent these limitations and measure a complete description of an unknown beam as a set of spectrally, temporally, and polarisation resolved spatial state density matrices. Each density matrix slice resolves the spatial complex amplitude of multiple mutually incoherent fields, which over several slices reveals the spectral or temporal evolution of these fields even when fields spectrally or temporally overlap. We demonstrate these features by characterising the spatiotemporal and spatiospectral output of a vertical-cavity surface-emitting laser.
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http://dx.doi.org/10.1038/s41467-022-31814-2 | DOI Listing |
J Struct Biol
January 2025
CEMES-CNRS, Université de Toulouse, I3EM Team, 29 rue JeanneMarvig B.P, 94347 31055 Toulouse, France. Electronic address:
Transmission electron microscopy, especially at cryogenic temperature, is largely used for studying biological macromolecular complexes. A main difficulty of TEM imaging of biological samples is the weak amplitude contrasts due to electron diffusion on light elements that compose biological organisms. Achieving high-resolution reconstructions implies therefore the acquisition of a huge number of TEM micrographs followed by a time-consuming image analysis.
View Article and Find Full Text PDFJ Environ Manage
January 2025
Management Science Institute, Hohai University, Nanjing, 210098, China.
Residents' satisfaction perceptions of ecosystem services (ESs) are essential for the ecological protection and high-quality development of the Yellow River Basin (YRB). Existing studies lacks large-scale survey of local residents' satisfaction perception at urban scale within river basins, and has not effectively explored the matching relationship between the ESs supply and the perceptions of local residents. To address this gap, this study develops a database on nine ESs supply and individual perceptions of the YRB, constructs a comprehensive framework to quantify the matching of ESs supply and local residents' satisfaction perceptions, and proposes targeted strategy.
View Article and Find Full Text PDFJ Hazard Mater
January 2025
Department of Civil, Urban, Earth, and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea. Electronic address:
The source-receptor relationship of atmospheric mercury is a critical environmental concern. However, comprehensive evaluations of mercury pollution based on spatially resolved and time-averaged data have not yet been conducted in Korea. In this study, the spatio-temporal variations of total gaseous mercury (TGM) and mercury isotopes were examined using passive air samplers at 30 sites in Ulsan over one year.
View Article and Find Full Text PDFEnviron Int
January 2025
School of Environmental Science and Engineering, Tianjin University, Tianjin 300354, China. Electronic address:
Micro-and-nano plastics (MNPs) are pervasive in terrestrial ecosystems and represent an increasing threat to plant health; however, the mechanisms underlying their phytotoxicity remain inadequately understood. MNPs can infiltrate plants through roots or leaves, causing a range of toxic effects, including inhibiting water and nutrient uptake, reducing seed germination rates, and impeding photosynthesis, resulting in oxidative damage within the plant system. The effects of MNPs are complex and influenced by various factors including size, shape, functional groups, and concentration.
View Article and Find Full Text PDFNeural Netw
December 2024
Department of Earth Science and Engineering, Imperial College London, Prince Consort Road, London SW7 2BP, UK; Centre for AI-Physics Modelling, Imperial-X, White City Campus, Imperial College London, W12 7SL, UK.
Machine learning (ML) has benefited from both software and hardware advancements, leading to increasing interest in capitalising on ML throughout academia and industry. There have been efforts in the scientific computing community to leverage this development via implementing conventional partial differential equation (PDE) solvers with machine learning packages, most of which rely on structured spatial discretisation and fast convolution algorithms. However, unstructured meshes are favoured in problems with complex geometries.
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