基于MEMS技术的离子图象和细胞传感器的研究

【作者】 贺慧琦；

【导师】 王平；

【作者基本信息】 浙江大学 ， 生物医学工程， 2003， 硕士

【摘要】 基于MEMS技术的生化传感器，是目前传感器发展的趋势，它有着集成度高，灵敏度高，进样量少和成本低等特点。本文研究了两种与微加工工艺相结合的微传感器阵列——用于液态成分分析的离子图象微传感器阵列和用于检测细胞生理活动的场效应管细胞传感器。 离子图象微传感器阵列可对pH=1-12的溶液样本和包括Cu²⁺，Fe²⁺，Fe³⁺，Ca²⁺，Zn²⁺,Mg²⁺在内的6种金属离子实现定性、定量的测量。该传感器阵列是一种将传统滴定方法与微加工技术相结合的新的尝试，采用了阵列设计的思想，消除了分离传感器选择性不高、交叉敏感的缺点，大大提高了传感器的灵敏度和检测下限。 实验中我们采用传统的湿法腐蚀工艺在P型硅基底上制备出微井和微通道，将吸附了对pH和金属离子敏感的染料和荧光物质的微球做为化学传感器放入微井中，并记录下指示刑遇到特定离子时发生的颜色变化，可以一次性检测多个指标。此外，本文还采用三种多变量数据分析方法来处理从微球中获取的原始数据并预测结果，研究成果表明采用主元分析（PCA）可成功的将六种金属离子实现聚类，偏最小二乘法（PLS）和神经网络（ANN）都可以定量计算pH值，且PLS比ANN迭代次数少、精度高。 为了实现微传感器的自动进样，我们设计了一种微泵，将进样系统和反应腔体相结合，采用有限元分析软件ANSYS对微传感器的动力学特性进行进行了静力场、动力场和流体场的综合分析，模拟了这些微结构器件的工作状况，优化了该传感器的设计。 基于场效应管阵列的细胞传感器是一种无损测量细胞动作电位的器件，可以定位到单个的细胞，实现实时检测外界电刺激和药物作用下的膜电位的改变。本文采用传统IC工艺方法设计并制备了场效应管阵列，用化学方法（涂附多聚鸟氨酸和层粘素）处理硅片引导心肌细胞的生长，并测试细胞在兴奋剂和抑制剂作用下的动作电位的变化。 通过进一步的研究可以将离子图象传感器阵列与场效应管细胞传感器相结合，从而确定引起膜电位变化的具体离子种类，了解外界电刺激和药物作用下细胞膜电位和离子流的关系，进而明确药物对于离子通道的作用机理。更多还原

【Abstract】 The biochemical sensor based on MEMS technology is developing quickly nowadays. It has the advantages of higher integration, higher sensitivity, less sample volume and lower cost. In thispaper, two microsensor arrays are studied------the ion image sensor array used for liquidcomponents analysis, and the Field Effect Transistor (FET) array used for detecting the active potential (AP) of living cell.The ion image micro sensors array can detect the pH value ranging from 1 to 12 and 6 kinds of metal ions, including Cu2*, Fe2+, Fe3+, Ca2+, Zn2+ and Mg2+ qualitatively and quantitatively. It combines the traditional titration method with microfabcrication technology. Also the idea of "array" is adopted, which can reduce the disadvantage of cross-sensitivity caused by the discrete selective sensor and has the advantages of significantly lower detection limit.In the experiments, we fabricate the microwells and microchannels on a P type silicon wafer with wet etching technology and deposite the microbeads with pH and metal indicators absorbed on the surface of them in the microwells. The image information under different solution samples is recorded in computer through microscope for further data processing and recognition. Moreover, three multivariate data analysis methods are suggested in the paper to treat the raw data, acquired from the microbeads, and to predict the results. The study demonstrates that the principal component analysis (PCA) is capable of classifying 6 kinds of cations with success, both Partial Least-Squares regression (PLS) and Artificial Neural Network (ANN) can be used to compute the pH value quantitatively and, furthermore, the PLS possesses the advantages of less iteration steps than ANN.In order to realize the auto sampling of the microsensor system, a micro pump is designed to integrated with the reaction chamber. The FEA (Finite Element Analysis) softwarepackage-----ANSYS is introduced to simulate the static, dynamic and fluid fields of the microdevice for optimizing the sensor system.The cell-based biosensor using FET array provides a non-invasive way for recording the active potention real time, and it can be reach to a single cell. The sensors array is designed and fabricated using traditional 1C technology. Poly-Left-Ornithine and Laminine are used as biological immobilization for inducing the growth of cardiac cells and the AP is measured underVthe treatment of the different drugs.Moreover, the ion image micro sensor array will be combined with the FET cell based sensor for studying the relationship between membrane potential and the influx /efflux of ions under the circumstance of outside stimulation and drug treatment. Then the mechanism of ion channels can be explored in order to investigate the application of this noninvasive measure system in the field of pathology analysis and drug screening.更多还原