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Conventional versus singlet-triplet Kondo effect in Blatter radical molecular junctions: Zero-bias anomalies and magnetoresistance
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Novel 1 H/ 19 F double‐tuned coil using an asymmetrical butterfly coil
No full textBackground: Fluorine-19 (19F) magnetic resonance imaging (MRI) is a non-invasive imaging tool for the targeted application of fluorinated agents, such as cell tracking, and for the demonstration of oximetry. However, as the SNR of 19F is significantly weaker than that of proton (1H) imaging, the 19F coil must be combined with 1H coils for anatomical co-registration and B0 shimming. This is difficult due to the strong coupling between the coils when they are in proximity, and is problematic since the Larmor frequency of 19F is 94% that of 1H, further increasing the potential for coupling between the 1H and 19F elements.Purpose: Conventional double-tuned coil methods tend to generate loss compared to single-tuned reference coils. The asymmetrical butterfly coil has a split resonance peak, which can cover frequencies of 1H and 19F without losses arising from lossy traps or switching circuits. In this study, the use of an asymmetrical butterfly coil was evaluated for 1H/19F applications.Methods: To increase quadrature efficiency at both the 1H and 19F frequencies, the left and right loops of the butterfly coil were tuned asymmetrically. The coil's tuning and performance were evaluated in simulations and MR measurements, and the results were compared to a dimension-matched single-tuned loop coil.Results: The split resonance peak of the asymmetrical butterfly coil successfully spanned the 19F to 1H frequency. It operated with higher quadrature efficiency at both 1H and 19F frequencies and demonstrated superior receive sensitivity and SNR compared to the dimension-matched single-tuned loop coil.Conclusions: The split resonance peak of the asymmetrical butterfly coil supported both 1H and 19F frequencies, delivering a higher SNR than that of the single-tuned loop coil. Since the asymmetrical butterfly coil can cover ¹H and ¹⁹F frequencies without loss and provides higher efficiency than the reference single-tuned coil, it can be effectively utilized for ¹H/¹⁹F MRI applications
Experimental study on interaction of KCl with potassium silicates and re-assessment of the K2O-SiO2 system
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Water vapour isotopes over West Africa as observed from space: which processes control tropospheric H 2 O ∕ HDO pair distributions?
No full textThe West African Monsoon (WAM) is crucial for rainfall in West Africa, impacting socio-economic conditions. Its complexity arises from interactions between large-scale circulation, convective dynamics, and microphysical processes, making it challenging to disentangle individual contributions to the hydrological cycle.Recent advances in retrieving the isotopic composition of tropospheric water vapour from space promote the paired analysis of H2O and HDO to study atmospheric moisture pathways and processes. Using data from the satellite instruments IASI (Infrared Atmospheric Sounding Interferometer), AIRS (Atmospheric Infrared Sounder) and TROPOMI (Tropospheric Monitoring Instrument), along with the IMERG (Integrated Multi-Satellite Retrievals for GPM) precipitation product, we analyse the variability of H2O and HDO (given as δD) over West Africa at convective and seasonal scales. Key findings include the following: (1) monsoon convection over the Sahel induces an anti-correlation between H2O and δD in the mid-troposphere. This is due to dry intrusions from the Saharan upper troposphere into Sahelian squall lines, fostering rain evaporation and mid-tropospheric δD depletion. (2) Over the Guinea coast, convective precipitation is associated with moist and enriched signals, with surface evaporation from the tropical Atlantic reducing rain evaporation and δD depletion. (3) During the Sahelian monsoon peak, an anti-correlation between precipitation and δD forms year to year, indicating the amount effect in tropospheric water vapour. (4) In the Sahelian winter, when precipitation is minimal, {H2O, δD} signals point to mixing of dry air masses of different origins.This study is the first to apply comprehensive isotopic datasets from IASI, TROPOMI and AIRS to the WAM, demonstrating the utility of satellite-based {H2O, δD} pairs in detecting impacts of microphysical and dynamical processes on water vapour isotopic composition
LiberTEM/LiberTEM: 0.14.2
No full textHomepage: https://libertem.github.io/LiberTEM/GitHub repository: https://github.com/LiberTEM/LiberTEM/PyPI: https://pypi.org/project/libertem/ LiberTEM is an open source platform for high-throughput distributed processing of large-scale binary data sets and live data streams using a modified MapReduce programming model. The current focus is pixelated scanning transmission electron microscopy (STEM) and scanning electron beam diffraction data. MapReduce-like processing allows to specify an algorithm through two functions: One function that is mapped on portions of the input data, and another function that merges (reduces) a partial result from this mapping step into the complete result. A wide range of TEM and 4D STEM processing tasks can be expressed in this fashion, see Applications. The UDF interface of LiberTEM offers a standardized, versatile API to decouple the mathematical core of an algorithm from details of data source, parallelism, and use of results. Mapping and merging can be performed in any order and with different subdivisions of the input data, including running parts of the calculation concurrently. That means the same implementation can be used in a wide range of modalities, including massive scaling on clusters. Since each merge step produces an intermediate result, this style of processing is suitable for displaying live results from a running calculation in a GUI application and for processing live data streams. A closed-loop feedback between processing and instrument control can be realized as well. See User-defined functions for more details on the LiberTEM UDF interface. The LiberTEM back-end offers high throughput and scalability on PCs, single server nodes, clusters and cloud services. On clusters it can use fast distributed local storage on high-performance SSDs. That way it achieves very high aggregate IO performance on a compact and cost-efficient system built from stock components. All CPU cores and CUDA devices in a system can be used in parallel. LiberTEM is supported on Linux, Mac OS X and Windows. Other platforms that allow installation of Python 3.7+ and the required packages will likely work as well. The GUI is running in a web browser. Installation The short version: source ~/libertem-venv/bin/activate (libertem-venv) python -m pip install 'libertem[torch]' # optional for GPU support # See also https://docs.cupy.dev/en/stable/install.html (libertem-venv) python -m pip install cupy Please see our documentation for details! Alternatively, to run the LiberTEM Docker image: singularity exec docker://ghcr.io/libertem/libertem /venv/bin/libertem-server Deployment for offline data processing on a single-node system for a local user is thoroughly tested and can be considered stable. Deployment on a cluster is experimental and still requires some additional work, see Issue #105. Back-end support for live data processing is still experimental as well, see https://github.com/LiberTEM/LiberTEM-live. Applications Since LiberTEM is programmable through user-defined functions (UDFs), it can be used for a wide range of processing tasks on array-like data and data streams. The following applications have been implemented already: Virtual detectors (virtual bright field, virtual HAADF, center of mass , custom shapes via masks) Analysis of amorphous materials Strain mapping Off-axis electron holography reconstruction Single Side Band ptychography Some of these applications are available through an interactive web GUI. Please see the applications section of our documentation for details! The Python API and user-defined functions (UDFs) can be used for complex operations such as arbitrary linear operations and other features like data export. Example Jupyter notebooks are available in the examples directory. If you are having trouble running the examples, please let us know by filing an issue or by joining our Gitter chat. LiberTEM is suitable as a high-performance processing backend for other applications, including live data streams. Contact us if you are interested! LiberTEM is evolving rapidly and prioritizes features following user demand and contributions. Currently we are working on live data processing, improving application support for sparse data and event-based detectors, performance improvements for GPU processing, and implementing analysis methods for various applications of pixelated STEM and other large-scale detector data. If you like to influence the direction this project is taking, or if you'd like to contribute, please join our gitter chat and our general mailing list. File formats LiberTEM currently opens most file formats used for pixelated STEM. See our general information on loading data and format-specific documentation for more information! Raw binary files NumPy .npy binary files Thermo Fisher EMPAD detector files Quantum Detectors MIB format Nanomegas .blo block files Direct Electron DE5 files (HDF5-based) and Norpix SEQ files for DE-Series detectors Gatan K2 IS raw format Stacks of Gatan DM3 and DM4 files (via openNCEM) Single-file Gatan DM4 scans when saved using C-ordering FRMS6 from PNDetector pnCCD cameras (currently alpha, gain correction still needs UI changes) FEI SER files (via openNCEM) MRC (via openNCEM) HDF5-based formats such as HyperSpy files, NeXus and EMD TVIPS binary files Sparse data in Raw CSR (compressed sparse row) format, as is possible to generate from event-based detectors Please contact us if you are interested in support for an additional format! Live processing and detectors (experimental) See LiberTEM-live! License LiberTEM is licensed under GPLv3. The I/O parts are also available under the MIT license, please see LICENSE files in the subdirectories for details. Acknowledgements We are very grateful for your continuing support for LiberTEM! See the acknowledgement page for a list of authors and contributors to LiberTEM and its subprojects. See also our info on funding and industry partners