用於次ppm級氨氣偵測的超薄氮化銦感測器

Ultrathin InN-based Sensor for Sub-ppm Ammonia Detection

胡萬柏(Wan-Po Hu)

指導教授 ： 葉哲良

國立清華大學/電機資訊學院/電子工程研究所/碩士(2012年)

摘要

肝癌經常穩居國人十大死因的榜首。研究發現，慢性肝炎患者呼氣中可測得大於0.7 ppm的氨氣氣體濃度，因此人體呼氣中氨氣濃度的偵測也就益發重要。本文利用InN超薄膜(~10 nm)製備電阻式氣體感測器，偵測sub-ppm等級的氨氣濃度，並使用曲線擬合（curve fitting）方式分辨氨氣濃度，試圖建立肝臟疾病快速檢測模式。
實驗中先比較裸面氮化銦與表面白金（Pt）催化氮化銦感測器之差異，在背景氣體為合成空氣（21 % O2 + 79 % N2）的環境下，裸面氮化銦感測器在200 °C的操作溫度下對10 ppm的氨氣產生19 %的電流變化率，簡易定義下的反應時間為 1180秒；而Pt-InN感測器產生32 %的電流變化率，反應時間為922 秒。發現鍍上白金催化層可使反應時間縮短1.3倍，電流變化率增加1.7倍，且電流變化率與氨氣濃度（0.2 ppm – 10 ppm）的對數呈現線性關係，其斜率為17.76。
進一步探討Pt-InN感測器之反應，由於人體呼氣中含有氧氣、二氧化碳和氨氣，且氣體吸附反應速率與活化能和反應物濃度相關，因此探討上述氣體之活化能。在氮氣的環境中，將元件操作在不同的溫度區間，可推導出氧氣活化能為0.79 eV、二氧化碳活化能為0.45 eV、氨氣活化能為0.33 eV。為使感測器對氣體量測的動態分析更加精確，本文使用氣體吸附與脫離的推導公式，對量測的電流進行曲線擬合，重新定義反應時間，並探討吸附反應時間與濃度的關係。此外本文使用兩種方式區分氣體種類和其濃度：1. 時間與電流變化率之動態變化關係 2. 溫度調變結合雷達圖（radar chart）之穩態變化關係。在模擬人體呼氣濃度範圍下：氧氣(16 %–18 %)、二氧化碳(3 %–5 %)，成功使用曲線擬合之方式計算出腔體內的氧氣、二氧化碳和氨氣濃度。目前Pt-InN感測器最低可偵測到0.06 ppm濃度之氨氣，顯示元件對氨氣有極高的靈敏度，能成功達到肝病檢測之目的。

關鍵字

氮化銦 ； 氨氣

並列摘要

Liver cancer is usually the No. 1 of the top ten causes of death in Taiwan. Many studies have found that the breath ammonia level is significantly higher in chronic patients with liver disease（> 0.7 ppm）than that in normal people. Therefore, the detection of ammonia concentration in exhaled breath becomes more and more important. In this work, we used ultrathin indium nitride (InN) epilayer（~10 nm）to fabricate gas sensors for the detection of ammonia with sub-ppm level. Furthermore, we used curve fitting to distinguish different ammonia concentration and tried to establish a method of rapid diagnosis for liver disease.
Firstly, ultrathin InN-based gas sensors with and without a thin catalytic platinum（Pt）layer atop have been compared. For the bare InN sensor, the current variation of 10 ppm ammonia in air ambience (21 % O2 + 79 % N2) at 200 °C is 19 % and the simplified response time is 1180 s. For the Pt-coated InN sensor, the sensor shows a higher current variation ratio of 32 % and a shorter response time of 922 s under the same gas exposure environment. Compared with the results of these two devices, we found that catalytic Pt layer can shorten the simplified response time by 1.3 times and increase the current variation ratio by 1.7 times. In addition, there is a linear relationship between current variation and logarithmic concentration of ammonia（0.2 ppm – 10 ppm）, and its slope is 17.76.
We further analyzed the response of Pt-InN gas sensors. Since the main composition of human's breath is nitrogen, oxygen and carbon dioxide, and reaction rate of gas molecules is related to activation energy as well as reactant concentration, we discussed the activation energy of the above-mentioned gases. The activation energy can be derived by operating devices at different temperature in N2 ambience. The activation energy of oxygen, carbon dioxide, and ammonia is 0.79 eV, 0.45 eV, and 0.33 eV respectively. In order to obtain more precise analysis of dynamic response of gas sensors, the derived formulas for gas adsorption and desorption were employed. We redefined the simplified response time by the curve fitting of measured current response and discussed the relation between response time and gas concentration. Besides, we used two different methods to distinguish different gases and concentrations: 1. Dynamic relationship between time and current variation, 2. Stable relationship between temperature modulation and radar chart. In the simulated concentration range of human breath (oxygen: 16 %－18 % and carbon dioxide: 3 %－5 %), we have achieved to calculate the concentration of oxygen ,carbon dioxide and ammonia. Currently, the lowest detectable response to ammonia of Pt-coated InN sensors is 0.06 ppm, which shows that the InN gas sensors have very high sensitivity to ammonia and can be applied to diagnose liver disease successfully.

並列關鍵字

InN ； Ammonia

參考文獻

[13] 黃仕泓, 柯正浩, "表面聲波感測器之前瞻研究," 物理雙月刊, 廿三卷六期, 512-518, (2004).

[4] S. Chakraborty, D. Banerjee, I. Ray, and A. Sen, "Detection of biomarker in breath: A step towards noninvasive diabetes monitoring," Current Science, 94, 237-242, (2008).

[8] W. M. Kwok, Y. C. Bow, W. Y. Chan, M. C. Poon, P. G. Han, and H. Wong, "Study of porous silicon gas sensor," 1999 Ieee Hong Kong Electron Devices Meeting, Proceedings, 80-83, (1999).

[1] B. Timmer, W. Olthuis, and A. van den Berg, "Ammonia sensors and their applications -

[3] S. Davies, P. Spanel, and D. Smith, "Quantitative analysis of ammonia on the breath of patients in end-stage renal failure," Kidney International, 52, 223-228, (1997).