487 research outputs found

    Readings of Portable UV Spectrum Analyzer Data Based on Raspberry Pi

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    A review of clinical use of surface-enhanced Raman scattering-based biosensing for glioma

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    Glioma is the most common malignant tumor of the nervous system in recent centuries, and the incidence rate of glioma is increasing year by year. Its invasive growth and malignant biological behaviors make it one of the most challenging malignant tumors. Maximizing the resection range (EOR) while minimizing the impact on normal brain tissue is crucial for patient prognosis. Changes in metabolites produced by tumor cells and their microenvironments might be important indicators. As a powerful spectroscopic technique, surface-enhanced Raman scattering (SERS) has many advantages, including ultra-high sensitivity, high specificity, and non-invasive features, which allow SERS technology to be widely applied in biomedicine, especially in the differential diagnosis of malignant tumor tissues. This review first introduced the clinical use of responsive SERS probes. Next, the sensing mechanisms of microenvironment-responsive SERS probes were summarized. Finally, the biomedical applications of these responsive SERS probes were listed in four sections, detecting tumor boundaries due to the changes of pH-responsive SERS probes, SERS probes to guide tumor resection, SERS for liquid biopsy to achieve early diagnosis of tumors, and the application of free-label SERS technology to detect fresh glioma specimens. Finally, the challenges and prospects of responsive SERS detections were summarized for clinical use

    The Research of a New Iteration of the Circular Algorithm

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    A surface-enhanced Raman scattering (SERS)-active optical fiber sensor based on a three-dimensional sensing layer

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    AbstractTo fabricate a new surface-enhanced Raman scattering (SERS)-active optical fiber sensor, the design and preparation of SERS-active sensing layer is one of important topics. In this study, we fabricated a highly sensitive three-dimensional (3D) SERS-active sensing layer on the optical fiber terminal via in situ polymerizing a porous polymer material on a flat optical fiber terminal through thermal-induced process, following with the photochemical silver nanoparticles growth. The polymerized polymer formed a 3D porous structure with the pore size of 0.29–0.81μm, which were afterward decorated with abundant silver nanoparticles with the size of about 100nm, allowing for higher SERS enhancement. This SERS-active optical fiber sensor was applied for the determination of 4-mercaptopyridine, crystal violet and maleic acid The enhancement factor of this SERS sensing layer can be reached as about 108. The optical fiber sensor with high sensitive SERS-active porous polymer is expected for online analysis and environment detection

    (S)-2-Amino-1-(pyrrolidinium-2-ylmeth­yl)pyridinium dibromide

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    In the title compound, C10H17N3 2+·2Br−, the pyrrolidinium ring displays an envelope conformation, with the flap N atom lying 0.564 (6) Å from the mean plane of the remaining four C atoms. The attached methyl­ene C atom, which connects the pyrrolidinium ring and the 2-amino­pyridine group, is displaced from the plane of the four pyrrolidinium C atoms by 0.811 (8) Å in the same direction as the pyrrolidinium N atom. The amine N lies on the opposite side of this plane

    HULL SHAPE OPTIMIZATION OF SMALL UNDERWATER VEHICLE BASED ON KRIGING-BASED RESPONSE SURFACE METHOD AND MULTI-OBJECTIVE OPTIMIZATION ALGORITHM

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    Small underwater vehicles have unique advantages in ocean exploration. The resistance and volume of a vehicle are key factors affecting its operation time underwater. This paper aims to develop an effective method to obtain the optimal hull shape of a small underwater vehicle using Kriging-based response surface method (RSM) and multi-objective optimization algorithm. Firstly, the hydrodynamic performance of a small underwater vehicle is numerically investigated using computational fluid dynamics (CFD) method and the value range of related design variables is determined. The mesh convergence is verified to ensure the accuracy of the calculation results. Then, by means of the Latin hypercube sampling (LHS) design of simulation, the Kriging-based RSM model is developed according to the relation between each design variable of the vehicle and the output parameters applied to the vehicle. Based on the Kriging-based RSM model, the optimal hull shape of the vehicle is determined by using Screening and MOGA. As results, the vehicle resistance reduces and volume increases obviously
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