Please use this identifier to cite or link to this item:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/93460
Title: | 離子敏感型場效電晶體設計於Troponin I感測之研究 An Implementation of Ion Sensitive Field Effect Transistors for Troponin I Detection |
Authors: | 滑凱茵 Hoi-Yan Wat |
Advisor: | 林致廷 Chih-Ting Lin |
Keyword: | 離子敏感型場效電晶體,心肌鈣蛋白I感測器,感測膜面積研究,感測膜材料對ISFET性能影響,表面改質, Ion-sensitive field-effect transistor,Troponin I sensor,Area impact on ISFET performance,Sensing membrane material effects on ISFET,Surface modification, |
Publication Year : | 2024 |
Degree: | 碩士 |
Abstract: | 隨著全球各國陸續進入高齡化的社會,醫療問題顯得日益重要,根據多項研究指出,心血管疾病佔據所有疾病死因之冠,如急性心肌梗塞等都是人類健康的一大威脅。
因此,本篇論文主要探討如何製作出一個有良好穩定度,精確又能快速檢驗的生物感測器。我們將從半導體元件設計為核心,採用不同結構、不同面積、並進一步針對感測膜材料進行談討。再者,我們使用表面改質的方法,使表面種上抗體,使元件能有效檢測出抗原的濃度。接著,進行選擇比測試,說明元件具有的專一性吸附的特性。最後進行穩定度測試,驗證何種感測膜能夠有最少的時間飄移,以減少元件所造成的誤差。 在這篇論文中,我們驗證了多種大小、不同材料的元件,得到Al2O3有良好的穩定性,同時最大面積的Al2O3感測膜靈敏度甚至能夠達到50.4 mV/decade 的Troponin I量測。若未來能進一步結合大數據的資料及AI模型的訓練,將能夠有效解決時間飄移的問題,使臨床上有更穩定的表現,實現快速,精準的醫療檢測器。 As global populations continue to age, healthcare issues become increasingly important. Numerous studies have identified cardiovascular diseases as the leading cause of mortality, presenting significant threats to human health, including acute myocardial infarction. This paper addresses the development of a stable, precise, and rapid biosensor. It focuses on semiconductor device design, exploring various structures and areas, and deep into discussions regarding sensing membrane materials. Additionally, surface modification techniques are employed to immobilize antibodies on the surface, enabling effective detection of antigen concentrations. Subsequent selectivity tests demonstrate the specificity of the device. Finally, stability tests are conducted to discover which sensing membrane exhibits minimal drift over time. Through this study, we validate multiple devices of varying sizes and materials, identifying Al2O3 exhibited excellent sensitivity and stability. Furthermore, the largest area Al2O3 sensing membrane demonstrates a sensitivity of up to 50.4 mV/decade for Troponin I measurement. We hope to have the integration of big data and AI model training in future research holds promise for effectively addressing drift issues, leading to more stable performance in clinical settings and facilitating rapid and accurate medical diagnostics. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/93460 |
DOI: | 10.6342/NTU202402246 |
Fulltext Rights: | 未授權 |
Appears in Collections: | 元件材料與異質整合學位學程 |
Files in This Item:
File | Size | Format | |
---|---|---|---|
ntu-112-2.pdf Restricted Access | 3.57 MB | Adobe PDF |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.