The miscibility of a series of binary blends such as polystyrene/poly(methyl methacrylate) (PS/PMMA), polystyrene/poly(vinyl chloride)(PS/PVC), poly(vinyl chloride)/poly(polymethyl methacrylate)(PVC/PMMA) and poly(ethylene-co-vinyl alcohol)/poly(lactide-co-glycolide acid) PEVAL/PLGA with equal ratios and poly(ethylene oxide)/poly(hydroxyl propyl methyl cellulose) (PEO/PHPMC) containing 30 and 70 wt% PEO, which were randomly chosen among the widely systems reported in the literature, was investigated by a new method based on a direct analysis in real-time coupled with time-of-flight mass spectrometry (DART-ToF-MS). To reach this goal these pairs of polymers and copolymers were prepared by solvent casting method. As a first step, the DSC technique was undertaken in this work to highlight the published results on the miscibility of these binary systems. The thermogravimetry analysis (TGA) was used to define the optimum decomposition temperature of these blends programmed for the study of miscibility using the DART-ToF-MS technique. The results obtained by this method based on the comparison of the nature of the fragments resulting from the isothermal decomposition of the blend with those of their pure components have been very effective in demonstrating the character of miscibility of these systems. Indeed, it was found that the PS/PMMA-50 and PS/PVC-50 blends were immiscible, PVC/PMMA-50 and PEVAL/PLGA-50 miscible, and the PEO/PHMC partially miscible. This method, which is rapid and uses a very small amount of sample (1-2 mg) can be extended in its application to other blends whose other methods used have shown their limits due to the intrinsic properties of the polymers involved.
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http://dx.doi.org/10.3390/polym14091644 | DOI Listing |
Comput Med Imaging Graph
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CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; National Key Laboratory of Kidney Diseases, Beijing 100853, China. Electronic address:
In clinical optical molecular imaging, the need for real-time high frame rates and low excitation doses to ensure patient safety inherently increases susceptibility to detection noise. Faced with the challenge of image degradation caused by severe noise, image denoising is essential for mitigating the trade-off between acquisition cost and image quality. However, prevailing deep learning methods exhibit uncontrollable and suboptimal performance with limited interpretability, primarily due to neglecting underlying physical model and frequency information.
View Article and Find Full Text PDFEur J Radiol
January 2025
Department of Medicine 3, Friedrich-Alexander-University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany; Institute of Medical Physics, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany. Electronic address:
Objectives: Contrast agents are frequently administered in computed tomography (CT) scans used for opportunistic screening of osteoporosis. The objective of this study is to compare the impact of contrast-related bone mineral density (BMD) increase between phantom-based and internal CT calibration techniques.
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Am J Surg
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Ellen Leifer Shulman and Steven Shulman Digestive Disease Center, Cleveland Clinic Florida, Weston, FL, USA. Electronic address:
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Surgery
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Division of Digestive and General Surgery, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan.
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J Breath Res
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School of Medicine and Pharmacy, Ocean University of China, 5 Yushan Rd, Qingdao, Shandong, 266003, CHINA.
Lung cancer is one of the most common malignancy in the world, and early detection of lung cancer remains a challenge. The exhaled breath condensate (EBC) from lung and trachea can be collected totally noninvasively. In this study, our aim is to identify differential metabolites between non-small cell lung cancer (NSCLC) and control EBC samples and discriminate NSCLC group from control group by orthogonal projections to latent structures-discriminant analysis (OPLS-DA) models.
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