Semiconductor Ammonia Sensor and Its Application in Human Expiratory Health Monitoring
Received date: 2024-07-10
Revised date: 2024-09-23
Online published: 2025-04-30
Supported by
Ningbo Key Scientific and Technological Project(NBSTI 2023Z021)
Human exhaled air has a close relationship with diseases,among which ammonia becomes a respiratory marker for diseases such as kidney disease. Traditional exhaled gas detection methods are mainly detected by gas chromatography,but the instrument is bulky and complex in operation. Emerging ammonia sensors,however,are garnering significant attention due to their portability,ease of integration,miniaturization,low cost,and simplicity of operation. This review systematically describes the working mechanism of ammonia gas sensors,sensor types,and common ammonia sensing materials. At the same time,it introduces the advantages of sensor array electronic nose technology over a single sensor,and puts forward the application research of ammonia sensors and electronic noses in diseases,aiming at the existing problems and prospects of ammonia gas sensors.
1 Introduction
2 Principe of semiconductor ammonia sensor
2.1 Quartz crystal microbalance ammonia sensor
2.2 Electrochemical ammonia sensor
2.3 Colorimetric ammonia sensor
2.4 Resistive ammonia sensor
3 Resistive ammonia sensing gas sensitive material
3.1 Metallic oxide
3.2 Conducting polymer
3.3 Carbon material
3.4 2D material
4 E-nose based on ammonia sensing
4.1 Eigenvalue extraction
4.2 Classical pattern recognition algorithm
4.3 Neural network
5 Applications of ammonia sensors in different diseases
5.1 Application of ammonia sensor in chronic kidney disease
5.2 Application of ammonia sensor in helicobacter pylori positive patients
6 Conclusion and outlook
Mingxia Feng , Jintian Qian , Dawu Lv , Wenfeng Shen , Weijie Song , Ruiqin Tan . Semiconductor Ammonia Sensor and Its Application in Human Expiratory Health Monitoring[J]. Progress in Chemistry, 2025 , 37(5) : 743 -757 . DOI: 10.7536/PC240704
表1 近年来气敏材料对氨气传感器的性能分析Table 1 Recently years performance analysis of ammonia sensor with different gas sensing materials |
Sensitive material | Operating temperature | Ammonia concentration | Responsivity | Response time | LOD(limit of detection) | Preparation method | Ref |
---|---|---|---|---|---|---|---|
PANI-Ge | room temperature | 50 ppm | 1098.6% | 72 s | - | In situ polymerization | 51 |
PANI-Rh-SnO2 | room temperature | 100 ppm | 13.6 | 113 s | 0.5 ppm | Electrospinning,in situ polymerization | 52 |
PANI-NiO | room temperature | 10 ppm | 43% | 149 s | 0.5 ppm | In situ polymerization | 25 |
BaFe12O19 | 306 ℃ | 1 ppm | 230% | 2.88 s | 0.2 ppm | Solid-state reaction method | 53 |
PANI-MoS2-Pt | room temperature | 50 ppm | 16 | 13 s | 250 ppb | Hydrothermal in situ polymerization | 39 |
PP/G/PANI | room temperature | 50 ppm | 250% | 114 s | 100 ppb | Dip coating,in situ polymerization | 54 |
PANI/HNTs | room temperature | 50 ppm | 257.41% | 10 ppb | In situ polymerization | 55 | |
PEDOT:PSS/MXene | room temperature | 100 ppm | 36.6% | 116 s | 10 ppm | In situ polymerization | 56 |
MXene-Ti3C2Tx | room temperature | 80 ppm | 214.7% | 122 s | 20 ppm | Molecular self-assembly/oxidative polymerization | 50 |
CeO2@PANI | room temperature | 100 ppm | 670% | 300 s | 50 ppb | In situ polymerization | 57 |
SnO2-PANI | room temperature | 100 ppm | 29.8 | 125 s | 10 ppb | Hydrothermal method,in situ polymerization | 58 |
WO3-PANI | room temperature | 100 ppm | 121% | 32 s | - | In situ polymerization | 59 |
NiO-PANI | room temperature | 10 ppm | 43% | 149 s | - | In situ polymerization | 25 |
ZnO@CNT | room temperature | 5 ppm | 12.3% | 5 s | - | Electrospinning,carbonization method | 60 |
p-PP/CNT/PANI | room temperature | 70 ppm | 452% | 93 s | 500 ppb | In situ polymerization | 61 |
CuBr/CeO2 | room temperature | 5 ppm | 68 | 210 s | 20 ppb | Electron beam evaporation | 62 |
PANI@SnO2 | room temperature | 100 ppm | 15.3 | - | - | Electrospinning,In situ polymerization | 63 |
PANI/(Cu-en) | room temperature | 100 ppm | 3.8 | 100 s | 2 ppm | Oxidative polymerization | 64 |
图2 (a)3% Al掺杂ZnO薄膜的FESEM图像,(b)3% Al掺杂 ZnO 传感器对 NH3 的瞬态响应[28],(c)MoS2/ZnO 纳米颗粒的FESEM图像,(d)MoS2/ZnO薄膜传感器在室温下对0.25~100 ppm氨气的电阻变化值[29]Fig.2 (a)FESEM image of 3% Al-doped ZnO thin film,(b)transient response of 3% Al-doped ZnO sensor to NH3[28],(c)FESEM image of MoS2/ZnO nanoparticles,(d)resistance change values of MoS2/ZnO thin film sensor at room temperature for ammonia concentrations ranging from 0.25 to 100 ppm[29] |
图3 (a)Sb掺杂SnO2的SEM图像,(b)SnO2/Sb传感器对不同氨气浓度的气体响应[30],(c)SnO2/WSe2 NPs的SEM图,(d)SnO2 /WSe2、WSe2和 SnO2薄膜传感器对不同浓度气体的响应[31]Fig.3 (a)SEM image of Sb-doped SnO2,(b)Gas response of SnO2/Sb sensor to different ammonia concentrations[30],(c)SEM image of SnO2/WSe2 NPs,(d)Gas response of SnO2/WSe2,WSe2 and SnO2 film sensors to different gas concentrations [31] |
图5 (a)PANI的SEM图像,(b)Pt/MoS2/PANI复合材料的 SEM图像[39],(c)Cu纳米颗粒负载PANI/MoO3结构示意图,(d)纯 PANI、PMN-20 和 Cu-3-PMN-20 传感器件对10~5000 ppb NH3 的响应曲线,(e)PANI/MoO3内部p-n结和肖特基结的气敏增强效应能带图[40]Fig.5 (a)SEM image of PANI,(b)SEM image of Pt/MoS2/PANI composite material [39],(c)Schematic diagram of Cu nanoparticles loaded on PANI/MoO3 structure,(d)Response curves of pure PANI,PMN-20,and Cu-3-PMN-20 sensor devices to 10~5000 ppb NH3,(e)Energy band diagram of gas sensing enhancement effect of internal p-n junction and Schottky junction in PANI/MoO3[40] |
图6 (a)原始和(b)装饰3 min的铟纳米颗粒的FESEM显微照片,(c)、(d)显示100 ppm NH3环境下的传感器响应曲线[41],图(e)为生物炭/SnO2的纵向FESEM显微照片,图(f)为生物炭和生物炭/SnO2对500 ppm NH3的响应曲线[42]Fig. 6 (a)FESEM images of pristine and(b)3 minutes decorated indium nanoparticles,(c),(d)sensor response curves in 100 ppm NH3[41],(e)longitudinal FESEM image of biochar/SnO2 and(f)response curves of biochar and biochar/SnO2 to 500 ppm NH3[42] |
图7 (a)5CC-COOH-Ti3C2Tx/PANI传感材料的SEM图像,(b)5CC-COOH-Ti3C2Tx/PANI 传感器在T=21 ℃,RH=55%环境下对不同浓度氨气的响应(c)5CC-COOH-Ti3C2Tx/PANI对于NH3的传感机理[50]Fig.7 (a)SEM image of 5CC-COOH-Ti3C2Tx/PANI sensing material,(b)Response of 5CC-COOH-Ti3C2Tx/PANI sensor to different ammonia concentrations under T=21 ℃,RH=55% environment,(c)Sensing mechanism of 5CC-COOH-Ti3C2Tx/PANI for NH3[50] |
图9 不同特征提取方法示意图:(a)气体响应相位划分,(b)SS[70]、VR和AUC[71],(c)FIT[72],(d)EMA,(e)10个传统的时间序列特征[74],(f)高度可比的时间序列分析框架工具包,将单个实验的时间序列转换为7642个统计特征的数组[75]Fig.9 Illustrates various feature extraction methods:(a)gas response phase partitioning,(b)SS[70],VR,and AUC[71],(c)FIT[72],(d)EMA,(e)ten traditional time series features[74],and(f)a highly comparable time series analysis framework toolkit that converts the time series of a single experiment into an array of 7642 statistical features[75] |
图10 (a)用气体采样袋收集呼出的空气,(b)CEN电子鼻区分NO、NH3和H2S,(c)PCA对健康样本(+)和肾脏疾病样本(-)的分类结果[78],(d)使用PC1和PC5对6个糙米品种进行分类[80]Fig.10 (a)Exhaled air collected using a gas sampling bag,(b)CEN electronic nose distinguishing NO,NH3,and H2S,(c)PCA classification results for healthy samples(+)and kidney disease samples(-)[78],(d)classification of six brown rice varieties using PC1 and PC5[80] |
图13 (a)卷积神经网络(CNN)的气体传感数据分析对目标气体进行实时分类和回归,(b)对6种气体(即空气、CO、NH3、CO2、CH4和 C3H6O)的混淆矩阵分类准确率[87],(c)1D CNN-LSTM/GRU架构[88]Fig.13 (a)Shows the real-time classification and regression of target gases using gas sensor data analysis with a Convolutional Neural Network(CNN),(b)displays the classification accuracy of the confusion matrix for six gases(air,CO,NH3,CO2,CH4,and C3H6O)[87],(c)depicts the 1D CNN-L90STM/GRU architecture[88] |
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