کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7145250 | 1462070 | 2015 | 10 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Nanoimprinted thrombin aptasensor with picomolar sensitivity based on plasmon excited quantum dots
ترجمه فارسی عنوان
پتاسنسور ترومبین نانو با پوزیتومیت با حساسیت پیکومولار بر اساس نقاط کوانتومی هیجان پلاسما
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
شیمی
شیمی آنالیزی یا شیمی تجزیه
چکیده انگلیسی
To achieve sensitive biomolecular detection, a gold nanohole array sensing chip was designed by plasmonic simulation and fabricated by high fidelity nanoimprinting technology. The gold nanohole array on a glass substrate serves as an optical antenna array, to produce localized surface plasmon resonance (LSPR) of 10-100 times stronger than the illumination light for quantum dot (QD) excitation. In this work, a robust sandwich bioassay of primary aptamer/thrombin/secondary aptamer with QD655 was established on the chip, to detect thrombin at a limit of detection (LOD) of 1Â ng/ml (27Â pM), with low non-specific binding even at the presence of 1000 times more concentrated bovine serum albumin (BSA). The conformation and QD enhancement mechanism of the sandwich assay were characterized by atom force microscopy (AFM) and dual polarization interferometry (DPI). This is the first time that DNA is surface functionalized on gold nanostructures for protein detection by plasmon enhanced QD emission. Considering the low-cost, easy regeneration and long shelf-life of the capturing aptamer, low non-specific binding and high sensitivity of the bioassay, as well as the inexpensive mass fabrication of the high quality chips, this novel aptasensor platform is particularly useful for a point-of-care system on medical diagnostics.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Sensors and Actuators B: Chemical - Volume 221, 31 December 2015, Pages 207-216
Journal: Sensors and Actuators B: Chemical - Volume 221, 31 December 2015, Pages 207-216
نویسندگان
Hong Yan Song, Ten It Wong, Shifeng Guo, Jie Deng, Christina Tan, Sergey Gorelik, Xiaodong Zhou,