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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 薛文証 | zh_TW |
| dc.contributor.advisor | Wen-Jeng Hsueh | en |
| dc.contributor.author | 紀羽真 | zh_TW |
| dc.contributor.author | Yu-Chen Chi | en |
| dc.date.accessioned | 2025-07-17T16:06:11Z | - |
| dc.date.available | 2025-07-18 | - |
| dc.date.copyright | 2025-07-17 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-07-14 | - |
| dc.identifier.citation | [1] X. T. Yin, J. Li, D. Dastan, W. D. Zhou, H. Garmestani, and F. M. Alamgir, Ultra-high selectivity of H2 over CO with a pn nanojunction based gas sensors and its mechanism, Sens. Actuators B Chem. 319, 128330 (2020).
[2] M. Boomashri, P. Perumal, K. V. Gunavathy, F. H. Alkallas, A. B. G. Trabelsi, M. Shkir, and S. AlFaify, Detection of ammonia gas at room temperature through Sb doped SnO2 thin films: Improvement in sensing performance of SnO2, Ceram. Int. 49, 1009610106 (2023). [3] Z. Feng, A. Gaiardo, M. Valt, B. Fabbri, D. Casotti, S. Krik, L. Vanzetti, M. D. Ciana, S. Fioravanti, S. Caramori, A. Rota, and V. Guidi, Investigation on sensing performance of highly doped Sb/SnO2, Sensors 22, 1233 (2022). [4] K. Derrar, M. Zaabat, N. Rouabah, R. Nazir, F. Hanini, A. Hafdallah, S. A. Khan, N. S. Alsaiari, K. M. Katubi, and K. M. Abualnaja, Preparation of Sb: SnO2 thin films and its effect on opto-electrical properties, J. Mater. Sci. Mater. Electron. 33, 10142-10153 (2022). [5] W. C. Huang, Y. Li, N. H. Chang, W. J. Hong, S. Y. Wu, S. Y. Liao, W. J. Hsueh, C. M. Wang, and C. Y. Huang, Highly stable and selective H2 gas sensors based on lightactivated a-IGZO thin films with ZIF-8 selective membranes, Sens. Actuators B Chem. 417, 136175 (2024). [6] A. Nasri, A. Boujnah, A. Boubaker, and A. Kalboussi, A smart gas sensor using machine learning algorithms: sensor types based on IED configurations, fabrication techniques, algorithmic approaches, challenges, progress, and limitations: a review, IEEE Sens. J. 23, 11336-11355 (2023). [7] J. Oh, S. H. Kim, M. J. Lee, H. Hwang, W. Ku, J. Lim, I. S. Hwang, J. H. Lee, and J. H. Hwang, Machine learning-based discrimination of indoor pollutants using an oxide gas sensor array: High endurance against ambient humidity and temperature, Sens. Actuators B Chem. 364, 131894 (2022). [8] P. G. Su, and M. C. Li, Recognition of binary mixture of NO2 and NO gases using a chemiresistive sensors array combined with principal component analysis, Sens. Actuator A Phys. 331, 112980 (2021). [9] Y. F. Lin, Z. C. Tseng, W. J. Hsueh, J. H. Chen, and C. Y. Huang, Room-Temperature Gas Identification via Photochemically Activated p-type CuCrO2 Nanostructured Films Using Machine Learning, ACS Appl. Nano Mater. 8, 189-199 (2025). [10] W. B. Soltan, S. Nasri, M. S. Lassoued, and S. Ammar, Structural, optical properties, impedance spectroscopy studies and electrical conductivity of SnO2 nanoparticles prepared by polyol method, J. Mater. Sci. Mater. Electron. 28, 6649-6656 (2017). [11] K. G. Godinho, A. Walsh, and G. W. Watson, Energetic and electronic structure analysis of intrinsic defects in SnO2, J. Phys. Chem. C 113, 439-448 (2009). [12] A. Soussi, R. Haounati, M. Taoufiq, S. Baoubih, Z. Jellil, A. Elfanaoui, R. Markazi, A. Ihlal, Investigating structural, morphological, electronic, and optical properties of SnO2 and Al-doped SnO2: A combined DFT calculation and experimental study, Physica B 690, 416242 (2024). [13] M. Kojima, H. Kato, A. Imai, and A. Yoshida, Electronic conduction of tin oxide thin films prepared by chemical vapor deposition, J. Appl. Phys. 64, 1902-1905 (1988). [14] B. T. Chiad, N. A. Ali, and N. T. Ali, Study the optical and structural properties of SnO2 films grown by (APCVD), J. Wasit Sci. Med. 7, 134-143 (2014). [15] M. R. Fadavieslam, H. Azimi-Juybari, and M. Marashi, Dependence of O2, N2 flow rate and deposition time on the structural, electrical and optical properties of SnO2 thin films deposited by atmospheric pressure chemical vapor deposition (APCVD), J. Mater. Sci.-Mater. Electron. 27, 921-930 (2016). [16] T. Serin, N. Serin, S. Karadeniz, H. Sarı, N. Tuğluoğlu, & O. Pakma, Electrical, structural and optical properties of SnO2 thin films prepared by spray pyrolysis, J. Non-Cryst. Solids 352, 209-215 (2006). [17] E. Savarimuthu, N. Sankarasubramanian, B. Subramanian, M. Jayachandran, C. Sanjeeviraja, and S. Ramamurthy, Preparation and characterisation of nanostructured tin oxide (SnO2) films by sol–gel spin coating technique, Surf. Eng. 22, 268-276 (2006). [18] H. J. Michel, H. Leiste, and J. Halbritter, Structural and electrical characterization of PVD-deposited SnO2 films for gas-sensor application, Sens. Actuators B Chem. 25, 568572 (1995). [19] T. I. Awan, S. Afsheen, and S. Kausar, Thin Film Deposition Techniques, 1st edn. ( Springer, Singapore, 2025) [20] R. J. Levy, Carbon monoxide pollution and neurodevelopment: a public health concern, Neurotoxicol. Teratol. 49, 31-40 (2015). [21] M. P. Diana, W. S. Roekmijati, and W. U. Suyud, Why it is often underestimated: historical study of ammonia gas exposure impacts towards human health. Paper presented at the 3rd International Conference on Energy, Environmental and Information System, Semarang, Indonesia, 14-15 August 2018. [22] Y. S. Najjar, Hydrogen safety: The road toward green technology, Int. J. Hydrogen Energy 38, 10716-10728 (2013). [23] R. Ehrlich, Effect of nitrogen dioxide on resistance to respiratory infection, Bacteriol. Rev. 30, 604-614 (1966). [24] H. J. Seo, R. H. Jeong, J. H. Boo, J. Song, and J. H. Boo, Study on chemical removal of nitric oxide (NO) as a main cause of fine dust (air pollution) and acid rain, Appl. Sci. Converg. Technol. 26, 218-222 (2017). [25] J. Han, H. Li, J. Cheng, X. Ma, and Y. Fu, Advances in metal oxide semiconductor gas sensor arrays based on machine learning algorithms, J. Mater. Chem. C 13, 42854303 (2025). [26] Y. Zhang, J. Zhao, T. Du, Z. Zhu, J. Zhang, and Q. Liu, A gas sensor array for the simultaneous detection of multiple VOCs, Sci. Rep. 7, 1960 (2017). [27] A. C. Romain, and J. Nicolas, Long term stability of metal oxide-based gas sensors for e-nose environmental applications: An overview, Sens. Actuators B Chem. 146, 502506 (2010). [28] C. G. Viejo, S. Fuentes, A. Godbole, B. Widdicombe, and R. R. Unnithan, Development of a low-cost e-nose to assess aroma profiles: An artificial intelligence application to assess beer quality, Sens. Actuators B Chem. 308, 127688 (2020). [29] D. Wang, L. Wang, H. Yin, G. Gu, Z. Lin, and W. Zhang, Graph-Driven Models for Gas Mixture Identification and Concentration Estimation on Heterogeneous Sensor Array Signals, IEEE Trans. Instrum. Meas. 2412, 13891 (2024). [30] S. Acharyya, B. Jana, S. Nag, G. Saha, and P. K. Guha, Single resistive sensor for selective detection of multiple VOCs employing SnO2 hollowspheres and machine learning algorithm: A proof of concept, Sens. Actuators B Chem. 321, 128484 (2020). [31] X. Zeng, M. Shahzeb, X. Cheng, Q. Shen, H. Xiao, C. Xia, Y. Xia, Y. Huang, J. Xu, and Z. Wang, An Enhanced Gas Sensor Data Classification Method Using Principal Component Analysis and Synthetic Minority Over-Sampling Technique Algorithms, Micromachines 15, 1501 (2024). [32] N. V. Duy, N. X. Thai, T. M. Ngoc, D. T. T. Le, C. M. Hung, H. Nguyen, M. Tonezzer, N. V. Hieu, and N. D. Hoa, Design and fabrication of effective gradient temperature sensor array based on bilayer SnO2/Pt for gas classification, Sens. Actuators B Chem. 351, 130979 (2022). [33] L. X. Ou, M. Y. Liu, L. Y. Zhu, D. W. Zhang, and H. L. Lu, Recent progress on flexible room-temperature gas sensors based on metal oxide semiconductor, Nano-Micro Lett. 14, 206 (2022). [34] X. Zhou, X. Cheng, Y. Zhu, A. A. Elzatahry, A. Alghamdi, Y. Deng, and D. Zhao, Ordered porous metal oxide semiconductors for gas sensing, Chin. Chem. Lett. 29, 405416 (2018). [35] H. J. Kim, and J. H. Lee, Highly sensitive and selective gas sensors using p-type oxide semiconductors: Overview, Sens. Actuators B Chem. 192, 607-627 (2014). [36] Y. Deng, Semiconducting Metal Oxides for Gas Sensing, 2nd edn. ( Springer, Singapore, 2023). [37] R. Shaik, R. K. Kampara, A. Kumar, C. S. Sharma, and M. Kumar, Metal oxide nanofibers based chemiresistive H2S gas sensors, Coordin. Chem. Rev. 471, 214752 (2022). [38] C. M. Bishop, and N. M. Nasrabadi, Pattern Recognition and Machine Learning, 1st edn. ( Springer, New York, 2006). [39] A. G. Smirnov, M. Berrendorf, Y. Y. Shprits, E. A. Kronberg, H. J. Allison, N. A. Aseev, I. S. Zhelavskaya, S. K. Morley, G. D. Reeves, M. R. Carver, and F. Effenberger, Medium energy electron flux in earth's outer radiation belt (MERLIN): A machine learning model, Space Weather 18, e2020SW002532 (2020). [40] J. Ali, R. Khan, N. Ahmad, and I. Maqsood, Random forests and decision trees, Int. J. Comput. Sci. Issues 9, 272 (2012). [41] G. Biau, and E. Scornet, A random forest guided tour, Test 25, 197-227 (2016). [42] A. H. Jahromi, and M. Taheri, A non-parametric mixture of Gaussian naive Bayes classifiers based on local independent features. Paper presented at 2017 Artificial Intelligence and Signal Processing Conference, Iran, Islamic Republic of 25 - 27 October 2017. [43] V. Kecman, Support Vector Machines: Theory and Applications, 1st edn. ( Springer, Berlin, Heidelberg, 2005). [44] S. Zhang, Challenges in KNN classification, IEEE Trans. Knowl. Data Eng. 34, 4663-4675 (2021). [45] S. Haireche, A. Boumeddiene,A. Guittoum, A. El Hdiy, and A. Boufelfel, Structural, morphological and electronic study of CVD SnO2: Sb films, Mater. Chem. Phys. 139, 871-876 (2013). [46] D. Hatem, and M. S. Belkaid, SnO2 thin films prepared by APCVD for organic solar cells application, Adv. Mat. Res. 685, 166-173 (2013). [47] P. Saikia, A. Borthakur, and P. K. Saikia, Structural, optical and electrical properties of tin oxide thin film deposited by APCVD method, Indian J. Phys. 85, 551-558(2011). [48] S. K. Sharma, D. S. Verma, L. U. Khan, S. Kumar, and S. B. Khan, Handbook of materials characterization, 1st edn. ( Springer, Cham, 2018). [49] A. Ali, Y. W. Chiang, and R. M. Santos, X-ray diffraction techniques for mineral characterization: A review for engineers of the fundamentals, applications, and research directions, Minerals 12, 205 (2022). [50] N. Jalili, and K. Laxminarayana, A review of atomic force microscopy imaging systems: application to molecular metrology and biological sciences, Mechatronics 14, 907-945 (2004). [51] F. A. Stevie, and C. L. Donley, Introduction to x-ray photoelectron spectroscopy, J. Vac. Sci. Technol. A 38, 063204 (2020). [52] E. Espid, and F. Taghipour, Facile synthesis and UV-activated gas sensing performance of Ag: ZnO nano-ellipsoids, ECS J. Solid State Sc. 7, Q3089 (2018). [53] S. S. Lekshmy, G. P. Daniel, and K. Joy, Microstructure and physical properties of sol gel derived SnO2: Sb thin films for optoelectronic applications, Appl. Surf. Sci. 274, 95-100 (2013). [54] W. W. Wang, Y. J. Zhu, and L. X. Yang, ZnO–SnO2 hollow spheres and hierarchical nanosheets: hydrothermal preparation, formation mechanism, and photocatalytic properties, Adv. Funct. Mater. 17, 59-64 (2007) [55] R. Ramanathan, S. Nagarajan, V. Bonu, P. Patel, S. Jamdar, H. C. Barshilia, and R. C. Mallik, Enhanced sensing performance of Sb-doped nanometer-thin SnO2 films toward CO and NH3 gases, ACS Appl. Nano Mater. 6, 7873-7886 (2023). [56] R. Ramanathan, S. Nagarajan, S. Sathiyamoorthy, B. Manavaimaran, H. C. Barshilia, and R. C. Mallik, A highly sensitive and room temperature ethanol gas sensor based on spray deposited Sb doped SnO2 thin films, Mater. Adv. 5, 293-305 (2024). [57] S. Y. Lee, and B. O. Park, Structural, electrical and optical characteristics of SnO2: Sb thin films by ultrasonic spray pyrolysis, Thin Solid Films 510, 154-158 (2006). [58] K. Shingange, Z. P. Tshabalala, O. M. Ntwaeaborwa, D. E. Motaung, and G. H. Mhlongo, Highly selective NH3 gas sensor based on Au loaded ZnO nanostructures prepared using microwave-assisted method, J. Colloid Interface Sci. 479, 127-138 (2016). [59] B. Benrabah, A. Bouaza, A. Kadari, and M. A. Maaref, Impedance studies of Sb doped SnO2 thin film prepared by sol gel process, Superlattice Microst. 50, 591-600 (2011). [60] M. Khabsa, A. Elmagarmid, I. Ilyas, H. Hammady, and M. Ouzzani, Learning to identify relevant studies for systematic reviews using random forest and external information, Mach. Learn. 102, 465-482 (2016). [61] I. Wickramasinghe, and H. Kalutarage, Naive Bayes: applications, variations and vulnerabilities: a review of literature with code snippets for implementation, Soft Comput. 25, 2277-2293 (2021). [62] G. H. John, and P. Langley, Estimating continuous distributions in Bayesian Classifiers. Paper presented at the Eleventh conference on Uncertainty in artificial intelligence, Montréal, Qué, Canada 18 - 20 August 1995. [63] E. Y. Boateng, J. Otoo, and D. A. Abaye, Basic tenets of classification algorithms K nearest-neighbor, support vector machine, random forest and neural network: A review, J. Data Anal. Inf. Process. 8, 341-357 (2020) | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/97811 | - |
| dc.description.abstract | 金屬氧化物半導體因其優異的氣體感測特性，在氣體感測領域中具發展潛力。然而，其氣體選擇性較差，難以區分不同的目標氣體。本研究使用自架設的常壓化學氣相沉積(APCVD)系統，製備濃度範圍為0 ~ 4 wt%的Sb摻雜SnO₂薄膜，形成同一基礎材料的氣體感測器陣列，並在室溫下照射紫外光以增強感測能力。經由單一響應特徵與機器學習演算法，能有效辨識 CO、NH₃、H₂和 NO₂這四種工業氣體。
不同的Sb摻雜濃度主要影響材料的電子濃度、氧空缺與薄膜的表面形貌，進而形成不同的感測器「指紋」，改變對不同氣體的響應。本研究採用主成分分析(PCA)提取數據的主要特徵，並利用隨機森林(RF)、貝氏分類(NB)、支援向量機(SVM)與 K近鄰演算法(KNN)這四種演算法進行氣體分類。模型訓練過程中，透過10折交叉驗證（10-Fold CV）來優化各演算法的超參數設定。結果顯示，僅使用三種Sb摻雜濃度的感測器，即可達到100%的分類準確度。顯示結合單一響應特徵與機器學習的Sb 摻雜SnO₂氣體感測器陣列，具備高效氣體識別的潛力，為未來智慧氣體感測技術提供可行方案。 | zh_TW |
| dc.description.abstract | Metal oxide semiconductors have shown great potential for development in the field of gas sensing due to their excellent gas sensing properties. However, their poor gas selectivity makes it difficult to distinguish between different target gases. In this study, a self-designed and built atmospheric pressure chemical vapor deposition (APCVD) system was employed to fabricate Sb-doped SnO₂ thin films with doping concentrations ranging from 0 to 4 wt%. A gas sensor array was consequently built with the same base material, and ultraviolet (UV) light irradiation at room temperature was applied to enhance the sensing performance. The combination of single-response features and machine learning algorithms enabled the effective identification of four industrial gases, namely CO, NH₃, H₂, and NO₂.
Different Sb doping concentrations mainly affect the material's electron concentration, oxygen vacancies, and surface morphology of the thin films, thereby creating distinct sensor "fingerprints" and altering their responses to various gases. Principal Component Analysis (PCA) was used to extract the key features from the sensing data. In addition, four supervised machine learning algorithms, namely Random Forest (RF), Naïve Bayes classifier (NB), Support Vector Machine (SVM), and K-Nearest Neighbors algorithm (KNN), were employed for gas classification. During model training, 10-fold cross-validation (10-Fold CV) was adopted to optimize the hyperparameters of each algorithm. The results demonstrate that using only three sensors with different Sb doping concentrations can achieve a classification accuracy of 100%. Furthermore, integrating the Sb-doped SnO₂ gas sensor array with single-response features and machine learning enables highly efficient gas recognition. Such integration represents a promising direction for the advancement of intelligent gas sensing systems. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-07-17T16:06:11Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-07-17T16:06:11Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 謝辭...................................................................................... i
摘要..................................................................................... ii Abstract................................................................................ iii 目次...................................................................................... v 圖次.................................................................................... vii 表次...................................................................................... x 第一章 緒論與文獻回顧....................................................................... 1 1.1 研究動機............................................................................... 1 1.2 材料特性............................................................................... 2 1.3 薄膜製程............................................................................... 5 1.4 工業氣體的危害......................................................................... 6 1.5 氣體感測陣列與機器學習應用.............................................................. 7 第二章 金屬氧化物半導體氣體感測器........................................................... 13 2.1 氣體感測機制.......................................................................... 13 2.2 量測參數.............................................................................. 16 2.2.1 靈敏度............................................................................. 16 2.2.2 響應............................................................................... 16 2.2.3 響應-恢復時間....................................................................... 17 第三章 機器學習........................................................................... 19 3.1 主成分分析............................................................................ 19 3.2 K折交叉驗證........................................................................... 20 3.3 隨機森林.............................................................................. 21 3.4 貝氏分類器............................................................................ 23 3.5 支援向量機............................................................................ 24 3.6 K近鄰演算法.......................................................................... 26 第四章 實驗流程........................................................................... 28 4.1 薄膜製程.............................................................................. 28 4.1.1 試劑特性............................................................................ 28 4.1.2 製程步驟............................................................................ 29 4.2 分析儀器.............................................................................. 30 4.2.1 熱場發射掃描式電子顯微鏡............................................................. 30 4.2.2 X光繞射儀.......................................................................... 31 4.2.3 原子力顯微鏡........................................................................ 32 4.2.4 X光光電子能譜儀.................................................................... 33 4.3 氣體感測量測系統...................................................................... 34 4.4 資料處理與機器學習.................................................................... 35 第五章 結果與討論......................................................................... 36 5.1 材料分析.............................................................................. 36 5.2 氣體感測特性.......................................................................... 39 5.3 氣體響應與主成分分析................................................................... 40 5.4 以機器學習演算法進行氣體識別........................................................... 44 第六章 結論與未來展望...................................................................... 49 6.1 結論................................................................................. 49 6.2 未來展望.............................................................................. 49 參考文獻.................................................................................. 50 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 選擇性 | zh_TW |
| dc.subject | 二氧化錫 | zh_TW |
| dc.subject | 常壓化學氣相沉積法 | zh_TW |
| dc.subject | 氣體感測器 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | machine learning | en |
| dc.subject | selectivity | en |
| dc.subject | gas sensor array | en |
| dc.subject | SnO₂ | en |
| dc.subject | APCVD | en |
| dc.title | 利用機器學習輔助之Sb摻雜SnO₂感測陣列於室溫下實現選擇性氣體偵測 | zh_TW |
| dc.title | Machine Learning-Assisted Selective Gas Detection at Room Temperature Using Sb-Doped SnO₂ Sensor Arrays | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.coadvisor | 黃俊穎 | zh_TW |
| dc.contributor.coadvisor | Chun-Ying Huang | en |
| dc.contributor.oralexamcommittee | 蕭惠心;彭朋群;陳正雄 | zh_TW |
| dc.contributor.oralexamcommittee | Hui-Hsin Hsiao;Peng-Chun Peng;Jeng-Shiung Chen | en |
| dc.subject.keyword | 二氧化錫,常壓化學氣相沉積法,機器學習,選擇性,氣體感測器, | zh_TW |
| dc.subject.keyword | SnO₂,APCVD,machine learning,selectivity,gas sensor array, | en |
| dc.relation.page | 56 | - |
| dc.identifier.doi | 10.6342/NTU202501684 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2025-07-15 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 工程科學及海洋工程學系 | - |
| dc.date.embargo-lift | N/A | - |
| 顯示於系所單位： | 工程科學及海洋工程學系 | |
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