A pentafluoropropionic acid-functionalized fluorescent metal-organic framework material (UiO-66-NH-PFPA) is prepared by a simple post-synthetic modification (PSM) strategy for the sensitive and selective detection of dichloran (DCN). The results of fluorescence experiments demonstrate that the sensitivity of UiO-66-NH-PFPA (limit of detection, LOD = 0.478 μM) to DCN is nearly 10.93 times higher than that of UiO-66-NH (LOD = 5.225 μM) and the material has good selectivity and anti-interference ability. After the addition of DCN, the blue fluorescence of UiO-66-NH-PFPA is obviously quenched. Therefore, the possible quenching mechanism is further discussed in combination with relevant experiments and density functional theory calculations. Moreover, the sensor is applied to the detection of DCN in fruit samples with a satisfactory recovery of 101.1-107.9%, which implies that UiO-66-NH-PFPA is expected to be a candidate material for the detection of DCN in food.
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http://dx.doi.org/10.1007/s00604-024-06938-5 | DOI Listing |
Mikrochim Acta
January 2025
Key Laboratory of Eco-Functional Polymer Materials of the Ministry of Education, Key Laboratory of Polymer Materials of Gansu Province, College of Chemistry and Chemical Engineering, Northwest Normal University, Lanzhou, 730070, Gansu, China.
A pentafluoropropionic acid-functionalized fluorescent metal-organic framework material (UiO-66-NH-PFPA) is prepared by a simple post-synthetic modification (PSM) strategy for the sensitive and selective detection of dichloran (DCN). The results of fluorescence experiments demonstrate that the sensitivity of UiO-66-NH-PFPA (limit of detection, LOD = 0.478 μM) to DCN is nearly 10.
View Article and Find Full Text PDFSci Rep
January 2025
Key Laboratory of Instrumentation Science, Dynamic Measurement of Ministry of Education, North University of China, Taiyuan, 030051, Shanxi, China.
This paper propose a significantly enhanced YOLOv8 model specifically designed for detecting tongue fissures and teeth marks in Traditional Chinese Medicine (TCM) diagnostic images. By integrating the C2f_DCNv3 module, which incorporates Deformable Convolutions (DCN), replace the original C2f module, enabling the model to exhibit exceptional adaptability to intricate and irregular features, such as fine fissures and teeth marks. Furthermore, the introduction of the Squeeze-and-Excitation (SE) attention mechanism optimizes feature weighting, allowing the model to focus more accurately on key regions of the image, even in the presence of complex backgrounds.
View Article and Find Full Text PDFJ Mater Sci Mater Med
January 2025
Department of Oral and Maxillofacial Surgery, Nagoya City University Graduate School of Medical Sciences, Nagoya, Aichi, Japan.
Osseointegration is essential for successful implant treatment. However, the underlying molecular mechanisms remain unclear. In this study, we focused on decorin (DCN), which was hypothesized to be present in the proteoglycan (PG) layer at the interface between bone and the titanium oxide (TiOx) surface.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Electrodiagnosis, The Affiliated Hospital to Changchun University of Traditional Chinese Medicine, Changchun, 130021, China.
Thyroid nodules are a common thyroid disorder, and ultrasound imaging, as the primary diagnostic tool, is susceptible to variations based on the physician's experience, leading to misdiagnosis. This paper constructs an end-to-end thyroid nodule detection framework based on YOLOv8, enabling automatic detection and classification of nodules by extracting grayscale and elastic features from ultrasound images. First, an attention-weighted DCN is introduced to enhance superficial feature extraction and capture local information.
View Article and Find Full Text PDFSensors (Basel)
November 2024
School of Mechatronics Engineering and Automation, Foshan University, Foshan 528225, China.
During the production process of inkjet printing labels, printing defects can occur, affecting the readability of product information. The distinctive shapes and subtlety of printing defects present a significant challenge for achieving high accuracy and rapid detection in existing deep learning-based defect detection systems. To overcome this problem, we propose an improved model based on the structure of the YOLOv5 network to enhance the detection performance of printing defects.
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