葡萄糖、尿素、過氧化氫皆為人體內重要的物種中的其中三種。本研究中，一自製震盪線路及微電腦介面的多頻道石英壓電生化感測器已成功地被建立，實驗室自行撰寫的訊號擷取程式亦可分別用來偵測待測樣品物種(葡萄糖酸、銨根離子、氧氣)之頻率訊號。此多頻道生化感測器裝置包括了一個四頻道的壓電系統(包括兩工作電極以及兩個參考電極)及一氧電極系統。
葡萄糖氧化酶、尿素分解酶及過氧化氫分解酶皆被成功地固定化在碳六十上，而後塗佈在一多孔性材質(SiO2)上，經固定化後之酵素依然保有其催化之活性，特別是在此多頻道石英壓電系統中。將碳六十及碳六十/大環胺醚塗佈於晶片上分別作為葡萄糖酸及銨根離子的吸附劑，藉由晶片上吸附劑吸附催化後的產物，則會造成震盪頻率的下降，進而可以推得產物吸附的多寡，之後換算目標待測物的注入濃度。
此多頻道生化感測器對於個別的目標待測物種有相當好的選擇性。模擬血液中干擾物(半乳糖、半胱胺酸、尿酸等)的存在，對整個壓電系統影響也不至於太大。經固定化之後的酵素，其活性在15天中皆可保持在90%以上。此自製多頻道偵測系統中，亦能有不錯的線性關係，R2值皆可在0.978以上，而最佳偵測下限為12.37μg/mL。利用自製的多頻道石英壓電感測系統可以用來作混合物之定性分析，利用簡單線性迴歸及多重線性迴歸(MLR)之分析方法，亦可成功的將單一頻道及多頻道之樣品作定量分析工作。

Glucose, urea and hydrogen peroxide are important bio-species in the human body. In this study, a multi-channel piezoelectric crystal bio-sensor with the home-made computer interface for data processing was prepared and employed to detect separately various bio-species, e.g. glucose, urea and H2O2. The multi-channel PZ system includes two working electrodes and two reference electrodes and one oxygen electrode. Various immobilized fullerene C60-enzymes, e.g. C60-glucose oxidase, C60-Urease and C60-catalase, were synthesized and applied to catalyze oxidation of glucose, decomposition of urea and H2O2, respectively in this piezoelectric bio-sensor system. The C60 and C60-Cryptand-22 coated piezoelectric crystals were used to detect various products of these enzyme-catalyzed reactions. The oscillated frequency decreased due to adsorption of product catalyzed by immobilized C60-enzyme on the coating material. The C60-enzyme coated PZ bio-sensor exhibited quiet good selectivity for these bio-species. Other interfering compounds e.q. cysteine, tyrosine, uric acid and galactose in sample solutions showed nearly no interference with these C60-enzyme coated PZ bio-sensor. The activity of immobilized C60-enzyme can be hold over 90% in 15 days. The home-made multi-channels Piezoelectric crystal bio-sensor showed good detection limit of 12.37μg/mL and R2 values of calibration curve were more than 0.978. The piezoelectric crystal bio-sensor system can distinguish individual components in the mixed samples. Furthermore, multivariate linear regression (MLR) was employed to make quantitative analysis for these biospecies.

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