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New complexes catena-(μ2 -nitrato-O,O')bis(piperidinedithiocarbamato)bismuth(III) (1) and tetrakis(μ-nitrato)tetrakis[bis(tetrahydroquinolinedithiocarbamato)bismuth(III)] (2) were synthesised and characterised by elemental analysis, FTIR spectroscopy and thermogravimetric analysis. The single-crystal X-ray structures of 1 and 2 were determined. The coordination numbers of the Bi(III) ion are 8 for 1 and ≥6 for 2 when the experimental electron density for the nominal 6s(2) lone pair of electrons is included. Both complexes were used as single-source precursors for the synthesis of dodecylamine-, hexadecylamine-, oleylamine and tri-n-octylphosphine oxide-capped Bi2 S3 nanoparticles at different temperatures. UV/Vis spectra showed a blueshift in the absorbance band edge characteristic of a quantum size effect. High-quality, crystalline, long and short Bi2 S3 nanorods were obtained depending on the thermolysis temperature, which was varied from 190 to 270 °C. A general trend of increasing particle breadth with increasing reaction temperature and increasing length of the carbon chain of the amine (capping agent) was observed. Powder XRD patterns revealed the orthorhombic crystal structure of Bi2 S3 .
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http://dx.doi.org/10.1002/chem.201602106 | DOI Listing |
Clin Nucl Med
September 2025
Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland.
Background: Non-small cell lung cancer (NSCLC) is a complex disease characterized by diverse clinical, genetic, and histopathologic traits, necessitating personalized treatment approaches. While numerous biomarkers have been introduced for NSCLC prognostication, no single source of information can provide a comprehensive understanding of the disease. However, integrating biomarkers from multiple sources may offer a holistic view of the disease, enabling more accurate predictions.
View Article and Find Full Text PDFSensors (Basel)
August 2025
College of Engineering, China Agricultural University, Beijing 100083, China.
Accurate and timely land monitoring is crucial for addressing global environmental, economic, and societal challenges, including climate change, sustainable development, and disaster mitigation. While single-source remote sensing data offers significant capabilities, inherent limitations such as cloud cover interference (optical), speckle noise (radar), or limited spectral information (LiDAR) often hinder comprehensive and robust characterization of land surfaces. Recent advancements in synergistic harmonization technology for land monitoring, along with enhanced signal processing techniques and the integration of machine learning algorithms, have significantly broadened the scope and depth of geosciences.
View Article and Find Full Text PDFSci Total Environ
August 2025
Artificial Intelligence and Mathematical Modeling lab (AIMMlab), Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada; Institute of Health Policy, Management and Evaluation (IHPME), University of Toronto, Toronto, ON, Canada; Department of Mathematics, University of Toronto
Methane (CH) is a significant short-term climate change contributor, but scientists face technical difficulties in accurately detecting and measuring methane and determining its precise locations. Traditional monitoring systems that utilize in-situ sensors and single-source satellite data experience multiple issues, including limited geographic coverage and difficulties with data retrieval accuracy and source identification. The paper introduces a new hybrid multi-source fusion framework that combines Sentinel-5P satellite data with ERA5 climate reanalysis data and geospatial intelligence from OpenStreetMap (OSM) and Google Earth Engine (GEE).
View Article and Find Full Text PDFJMIR Public Health Surveill
August 2025
Division of Healthcare Quality Promotion, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, 1600 Clifton Road, Atlanta, GA, 30329, United States, 1 4047183262.
The National Healthcare Safety Network (NHSN) of the Centers for Disease Control and Prevention (CDC) needed a modernized approach to manage resources containing standardized terminology that specify microorganism data submitted electronically for legacy reporting. Health care-associated infections (HAIs) reported to NHSN require the submission of data regarding specific microorganisms attributed to the patient's condition. Data on microorganisms submitted to the NHSN electronically must use the SNOMED CT terminology standard.
View Article and Find Full Text PDFSensors (Basel)
July 2025
Key Laboratory of Advanced Equipment Intelligent Manufacturing Technology of Yunnan Province, Kunming University of Science & Technology, Kunming 650500, China.
Industrial equipment fault diagnosis faces dual challenges: significant data distribution discrepancies caused by diverse operating conditions impair generalization capabilities, while underutilized spatio-temporal information from multi-source data hinders feature extraction. To address this, we propose a spatio-temporal collaborative perception-driven feature graph construction and topology mining methodology for variable-condition diagnosis. First, leveraging the operational condition invariance and cross-condition consistency of fault features, we construct fault feature graphs using single-source data and similarity clustering, validating topological similarity and representational consistency under varying conditions.
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