基于nanopipet的单颗粒电化学分析

It is of great significance to develop a new method for single particles analysis, which is of great importance to understand the relationship between the structure and properties of particles and thus to study their functions. Because of its simple and easy manipulation, the single particle detection based on electrochemical principle has attracted much more attention in recent years. In this project, we would like to develop a new electrochemical principle for single particle analysis based on nanopipette. The research contents include: establishing the principle and the modeling for the single particle electrochemical analysis; investigating the quantitative relationship between physical and chemical parameters of single particle (such as surface charge, size, morphology, concentration) as well as signal based on experimental results and simulation; development of electrochemical detection method for soft particles (such as cells and liposome); exploring the application of the developed method for real samples (such as serum) analysis. The implementation of this project not only can greatly promote the development of the new principles, new technologies and new methods of single particle electrochemical analysis, but also will effectively promote the interdiscipline subject between nanoscience and electroanalytical chemistry.

发展可用于单颗粒检测和分析的研究方法，对于了解颗粒结构和性能的关系，进而研究其功能具有非常重要的意义。基于电化学原理进行单颗粒检测由于其仪器简单易操控等特点近年来受到了人们越来越多的关注。本项目拟针对单颗粒电化学检测中存在的关键科学问题，利用nanopipet 良好的稳定性和低噪声等优势，系统地开展基于nanopipet的单颗粒电化学分析研究。研究内容包括：单颗粒电化学分析原理和模型建立；单颗粒物理化学参数（如表面电荷、尺寸、形貌、浓度等）定量关系建立和理论模拟；软颗粒（如细胞和脂质体等）电化学检测；方法在实际样品（如血清等）中的有效性评价。本项目的实施不仅可以大大促进单颗粒电化学分析新原理、新技术和新方法的发展，而且也将有效推进纳米科学和电分析化学的深度交叉和融合。

按照项目计划书的研究内容和研究计划，我们系统研究了单颗粒电化学检测的新模型，发展并建立了单颗粒物理化学参数（如表面电荷、尺寸、形貌、浓度等）的理论模型和定量关系；在此基础上，建立了软颗粒（如细胞和脂质体等）的定量检测新原理和新方法，并探索了方法在实际样品（如血清等）中的有效性。经过四年的努力，我们已圆满地完成了项目的研究内容，同时对基于离子传输的小分子分析和固相离子传输进行了探索研究，超额完成了项目预定的指标，并取得了一些具有重要科学意义和应用价值的研究成果。项目执行期间，共发表研究论文 18 篇，其中包括 J. Am. Chem. Soc. 1 篇，Angew. Chem. Int. Ed.3 篇，Chem. Sci. 1 篇，Anal. Chem. 3篇，ACS Sens 2篇，获得中国分析测试协会一等奖一项（排名第一）。

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