| Summary: | 碩士 === 國立清華大學 === 化學系 === 100 === A new conductive nano-composite of carbon nanotube (CNT) and gold nanobone (GNB), (CNT@GNB) synthesized via a facile approach was demonstrated herein. 1-Pyrenemethyl-carbamoyl-S-thioacid, prepared by conjugation of pyrene moiety and N-succinimidyl S-acetylthioacetate, followed by the deprotection with hydroxylamine hydrochloride, was used to bridge the outer wall of the CNT and GNB through π- π stacking and gold-sulfur bonding. The SEM microscopic analysis confirmed the successful hybridization of GNB on CNT, and the as-synthesized nano-composite was subsequently employed for fabricating the screen-printed electrodes as the detection substrate. It showed that the CNT@GNB nano-composite-modified electrode could be utilized in the development of non-enzymatic electrochemical sensors toward the determination of hydrogen peroxide and glucose, with the LODs of 0.80 μM and 1.32 mM, respectively. In addition, it could be further applied in developing a novel and sensitive electrochemical sandwich immunosensor for the detection of P-selectin with the aid of potassium ferrocyanide-encapsulated, anti-P-selectin-tagged liposomal biolabels as electrochemical signal probes. The immunorecognition of the sample P-selectin by the liposomal biolabels occurred on the surface of the electrodes; the release of potassium ferrocyanide from the bound liposomal biolabels significantly contributes to the increase in electrochemical signal, which was acquired in HCl solution at +0.32 V in SWV voltammetry mode. The resulting sigmoidally shaped dose-response curves possessed a linear dynamic working range from 1 ×10-13 to 1 ×10-5 g/mL. This liposome-based electrochemical immunoassay provides an amplification approach for detecting P-selectin, leading to a detection limit as low as 4.3 fg (equivalent to 5 μL of 0.85 pg/mL in solution). A commercially available ELISA kit was as a reference method to validate the developed assay through the analysis of mouse serum samples. Moreover, a strong correlation was observed between the two data sets as the R-squared value of 0.997 from the linear regression line.
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