Urea is an essential molecule usually detected using spectroscopy, particularly ultraviolet and visible spectroscopy (UV-vis). However, its detection represents a not always fully acknowledged issue. Its concentration dependency has raised questions about the reliability of the UV-vis results. Derivatization reactions, common alternatives to achieve accuracy and precision with UV-vis measurements, still represent an additional step in the measurement process. Besides the problems mentioned earlier, urea forms complex mixtures in aqueous mediums. Therefore, this work proposes to investigate the accuracy and precision of urea determination by UV-vis spectroscopy in the pure form and derivatized with -dimethylaminobenzaldehyde. The results show that UV-vis spectroscopy could not quantify urea in both forms with precision and accuracy. On the other hand, when applying multivariate curve resolution with alternating least squares (MCR-ALS) to the UV-vis data, the pure urea analytical signal is mathematically separated. Then, those parameters of merit were successfully achieved.
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http://dx.doi.org/10.1039/d3ay00249g | DOI Listing |
Biomed Phys Eng Express
January 2025
Mindanao Radiation Physics Center, MSU-Iligan Institute of Technology, Andres Bonifacio Street Tibanga, Iligan City, Lanao Norte, 9200, PHILIPPINES.
To accurately model and validate the 6 MV Elekta Compactlinear accelerator using the Geant4 Application for Tomographic Emission (GATE). In particular, this study focuses on the precise calibration and validation of critical parameters, including jaw collimator positioning, electron source nominal energy, flattening filter geometry, and electron source spot size, which are often not provided in technical documentation. Methods: Simulation of the Elekta Compact6 MV linear accelerator was performed using the Geant4 Application for Tomographic Emission (GATE) v.
View Article and Find Full Text PDFNanotechnology
January 2025
Xi'an Jiaotong University, xian ning west road 28#, xi'an, Xi'an, None Selected, 710049, CHINA.
Accurate and rapid diagnosis of traumatic brain injury (TBI) is essential for high-quality medical services. Nonetheless, the current diagnostic platform still has challenges in rapidly and accurately analysing clinical samples. Here, we prepared a highly stable, repeatable and sensitive gold-plated silver core-shell nanowire (Ag@AuNWs) for surface-enhanced Raman spectroscopy (SERS) metabolic fingerprint diagnosis of TBI.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Department of Primate Behavioral Ecology, Institute of Biology, Leipzig University, Leipzig 04103, Germany.
Biological relatedness is a key consideration in studies of behavior, population structure, and trait evolution. Except for parent-offspring dyads, pedigrees capture relatedness imperfectly. The number and length of identical-by-descent DNA segments (IBD) yield the most precise relatedness estimates.
View Article and Find Full Text PDFPLoS One
January 2025
Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huai'an, Jiangsu, China.
Accurate detection of fabric defects is crucial for quality control in the textile industry. However, the task of fabric defect detection remains highly challenging due to the complex textures and diverse defect patterns. To address the issues of inaccurate localization and false positives caused by complex textures and varying defect sizes, this paper proposes an improved YOLOv8-based fabric defect detection method.
View Article and Find Full Text PDFJ Thorac Imaging
September 2024
School of Computer Science and Engineering, The Hebrew University of Jerusalem.
Purpose: Radiological follow-up of oncology patients requires the detection of metastatic lung lesions and the quantitative analysis of their changes in longitudinal imaging studies. Our aim was to evaluate SimU-Net, a novel deep learning method for the automatic analysis of metastatic lung lesions and their temporal changes in pairs of chest CT scans.
Materials And Methods: SimU-Net is a simultaneous multichannel 3D U-Net model trained on pairs of registered prior and current scans of a patient.
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