研究生: |
王玲瑄 Wang, Ling-Hsuan |
---|---|
論文名稱: |
染料與顏料之拉曼光譜資料庫的建立與應用 Establishment and Application of a Raman Spectral Database for Common Dyes and Pigments |
指導教授: |
林震煌
Lin, Cheng-Huang |
口試委員: |
林震煌
Lin, Cheng-Huang 李君婷 Li, Chun-Ting 何佳安 Ho, Ja-An |
口試日期: | 2024/06/05 |
學位類別: |
碩士 Master |
系所名稱: |
化學系 Department of Chemistry |
論文出版年: | 2024 |
畢業學年度: | 112 |
語文別: | 中文 |
論文頁數: | 74 |
中文關鍵詞: | LabVIEW 、表面增強拉曼光譜 |
英文關鍵詞: | LabVIEW, surface-enhanced Raman spectroscopy (SERS) |
研究方法: | 實驗設計法 、 主題分析 |
DOI URL: | http://doi.org/10.6345/NTNU202401311 |
論文種類: | 學術論文 |
相關次數: | 點閱:76 下載:0 |
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本研究使用了兩種基於不同原理撰寫的光譜資料庫,分別為均方根誤差和交互相關係數原理,作為評估準確性和相關性的衡量指標。由於每次測量的訊號強度和位移間距都各不相同,因此通過對光譜進行正規化處理,成功解決了光譜不一致的問題,可以檢索和比對多種常見的有機染料和無機顏料。
為了應對不同狀態的顏料樣品,使用了三種不同的前處理方式。這些樣品通常呈現不同的形式,例如包裝好的水彩顏料、上色後的畫作以及化學合成的染料等,因此針對這些樣品採用了相應的檢測方法。透過使用不同的前處理方法,結合表面增強拉曼散射技術可以對一些無法直接使用拉曼光譜儀測量到的樣品進行分析。
除了驗證了這些方法的可行性之外,同時結合資料庫的使用,比較兩個資料庫的準確度,從而實現了更快速的鑑定結果,為顏料的鑑定提供了更多的選擇性。透過在實際樣品上的成功應用,證明了該資料庫在不同顏料和染料比對上的有效性,同時也展示了在表面增強拉曼技術方面的適用性。
Two spectral databases built on different principles, namely the root mean square error and the cross-correlation coefficient principles, were used as measures to evaluate accuracy and correlation. Since the signal intensity and displacement spacing of each measurement are different, the problem of spectral inconsistency was successfully solved by normalizing the spectra, allowing for the retrieval and comparison of a variety of common organic dyes and inorganic pigments.
In order to deal with pigment samples in different states, three different pre-treatment methods are provided. Considering that the pigment samples that need to be identified usually come in different forms, such as packaged watercolor paints, colored paintings, and chemically synthesized dyes, we provide corresponding detection methods for these samples. By using different pre-treatment methods, combined with surface-enhanced Raman scattering technology, some samples that cannot be measured directly using a Raman spectrometer can be analyzed.
In addition to verifying the feasibility of these methods, it also combines the use of databases to compare the accuracy of the two databases, thereby achieving faster identification results and providing more selectivity for the identification of artworks. The successful application on actual samples demonstrates the effectiveness of the database in comparing different pigments and dyes, and also demonstrates its applicability in surface-enhanced Raman technology.
[1] Tsiper, S.; Dicker, O.; Kaizerman, I.; Zohar, Z.; Segev, M.; Eldar, Y. C. Sparsity-Based Super Resolution for SEM Images. Nano Lett., 2017, 17 (9), 5437–5445.
[2] Powell, C. D.; Pisharody, L.; Thamaraiselvan, C.; Gupta, A.; Park, H.; Tesfahunegn, B. A.; Sharma, C. P.; Kleinberg, M. N.; Burch, R.; Arnusch, C. J. Functional Laser-Induced Graphene Composite Art. ACS Appl. Nano Mater., 2022, 5 (8), 11923–11931.
[3] Carbó, M. T. D.; Reig, F. B.; Adelantado, J. V. G.; Martínez, V. P. Fourier Transform Infrared Spectroscopy and the Analytical Study of Works of Art for Purposes of Diagnosis and Conservation. Analytica Chimica Acta, 1996, 330 (2), 207–215.
[4] Debnath, N. C.; Vaidya, S. A. Application of X-Ray Diffraction Technique for Characterisation of Pigments and Control of Paints Quality. Progress in Organic Coatings, 2006, 56 (2), 159–168.
[5] Juraviciene, E.; Kiuberis, J.; Beganskienė, A.; Senvaitiene, J.; Kareiva, A. XRD and FTIR Characterisation of Historical Green Pigments and Their Lead-Based Glazes. Chemija, 2014, 25, 199–205.
[6] Corradini, M.; de Ferri, L.; Pojana, G. Spectroscopic Characterization of Commercial Pigments for Pictorial Retouching. Journal of Raman Spectroscopy, 2021, 52 (1), 35–58.
[7] White, S. N. Laser Raman Spectroscopy as a Technique for Identification of Seafloor Hydrothermal and Cold Seep Minerals. Chemical Geology, 2009, 259 (3), 240–252.
[8] Bersani, D.; Lottici, P. P. Raman Spectroscopy of Minerals and Mineral Pigments in Archaeometry. Journal of Raman Spectroscopy, 2016, 47 (5), 499–530. https://doi.org/10.1002/jrs.4914.
[9] Nodari, L.; Ricciardi, P. Non-Invasive Identification of Paint Binders in Illuminated Manuscripts by ER-FTIR Spectroscopy: A Systematic Study of the Influence of Different Pigments on the Binders’ Characteristic Spectral Features. Heritage Science, 2019, 7 (1), 7.
[10] Fan, M.; Andrade, G. F. S.; Brolo, A. G. A Review on Recent Advances in the Applications of Surface-Enhanced Raman Scattering in Analytical Chemistry. Analytica Chimica Acta, 2020, 1097, 1–29.
[11] Lee, M.; Oh, K.; Choi, H.-K.; Lee, S. G.; Youn, H. J.; Lee, H. L.; Jeong, D. H. Subnanomolar Sensitivity of Filter Paper-Based SERS Sensor for Pesticide Detection by Hydrophobicity Change of Paper Surface. ACS Sens., 2018, 3 (1), 151–159.
[12] Sahin, F.; Celik, N.; Camdal, A.; Sakir, M.; Ceylan, A.; Ruzi, M.; Onses, M. S. Machine Learning-Assisted Pesticide Detection on a Flexible Surface-Enhanced Raman Scattering Substrate Prepared by Silver Nanoparticles. ACS Appl. Nano Mater., 2022, 5 (9), 13112–13122.
[13] Clark, R. J. H.; Franks, M. L. The Resonance Raman Spectrum of Ultramarine Blue. Chemical Physics Letters, 1975, 34 (1), 69–72.
[14] Osticioli, I.; Mendes, N. F. C.; Nevin, A.; Gil, F. P. S. C.; Becucci, M.; Castellucci, E. Analysis of Natural and Artificial Ultramarine Blue Pigments Using Laser Induced Breakdown and Pulsed Raman Spectroscopy, Statistical Analysis and Light Microscopy. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2009, 73 (3), 525–531.
[15] Rusu, R. D.; Simionescu, B.; Oancea, A. V.; Geba, M.; Stratulat, L.; Salajan, D.; Ursu, L. E.; Popescu, M. C.; Dobromir, M.; Murariu, M.; et al. Analysis and Structural Characterization of Pigments and Materials Used in Nicolae Grigorescu Heritage Paintings. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2016, 168, 218–229.
[16] Moretti, G.; Gervais, C. Raman Spectroscopy of the Photosensitive Pigment Prussian Blue. Journal of Raman Spectroscopy, 2018, 49 (7), 1198–1204.
[17] Ware, M. Prussian Blue: Artists’ Pigment and Chemists’ Sponge. J. Chem. Educ., 2008, 85 (5), 612.
[18] Basova, T. V.; Kiselev, V. G.; Schuster, B.-E.; Peisert, H.; Chassé, T. Experimental and Theoretical Investigation of Vibrational Spectra of Copper Phthalocyanine: Polarized Single-Crystal Raman Spectra, Isotope Effect and DFT Calculations. Journal of Raman Spectroscopy, 2009, 40 (12), 2080–2087.
[19] Butler, I. S.; Furbacher, R. J. Chemistry and Artists’ Pigments. J. Chem. Educ., 1985, 62 (4), 334.
[20] Bovill, A. J.; McConnell, A. A.; Nimmo, J. A.; Smith, W. E. Resonance Raman Spectra of .Alpha.-Copper Phthalocyanine. J. Phys. Chem., 1986, 90 (4), 569–575.
[21] Ali, M. B.; Barras, A.; Addad, A.; Sieber, B.; Elhouichet, H.; Férid, M.; Szunerits, S.; Boukherroub, R. Co2SnO4 Nanoparticles as a High Performance Catalyst for Oxidative Degradation of Rhodamine B Dye and Pentachlorophenol by Activation of Peroxymonosulfate. Phys. Chem. Chem. Phys., 2017, 19 (9), 6569–6578.
[22] Tomasini, E.; Costantini, I.; Careaga, V.; Landa, C. R.; Castro, K.; Madariaga, J. M.; Maier, M.; Siracusano, G. Identification of Pigments and Binders of a 17th Century Mural Painting (Bolivia). New Report on Pigments Associated with Andean Minerals. Journal of Cultural Heritage, 2023, 62, 206–216.
[23] Leona, M.; Decuzzi, P.; Kubic, T. A.; Gates, G.; Lombardi, J. R. Nondestructive Identification of Natural and Synthetic Organic Colorants in Works of Art by Surface Enhanced Raman Scattering. Anal. Chem., 2011, 83 (11), 3990–3993.
[24] Nicolaou, K. C.; Sorensen, E. J.; Winssinger, N. The Art and Science of Organic and Natural Products Synthesis. J. Chem. Educ., 1998, 75 (10), 1225.
[25] Stallmann, O. Use of Metal Complexes in Organic Dyes and Pigments. J. Chem. Educ., 1960, 37 (5), 220.
[26] Giustetto, R.; Llabrés i Xamena, F. X.; Ricchiardi, G.; Bordiga, S.; Damin, A.; Gobetto, R.; Chierotti, M. R. Maya Blue: A Computational and Spectroscopic Study. J. Phys. Chem. B, 2005, 109 (41), 19360–19368.
[27] Doménech, A.; Doménech-Carbó, M. T.; Vázquez de Agredos Pascual, M. L. Dehydroindigo: A New Piece into the Maya Blue Puzzle from the Voltammetry of Microparticles Approach. J. Phys. Chem. B, 2006, 110 (12), 6027–6039.
[28] Agrawal, G.; Samal, S. K. Raman Spectroscopy for Advanced Polymeric Biomaterials. ACS Biomater. Sci. Eng., 2018, 4 (4), 1285–1299.
[29] Bumbrah, G. S.; Sharma, R. M. Raman Spectroscopy – Basic Principle, Instrumentation and Selected Applications for the Characterization of Drugs of Abuse. Egyptian Journal of Forensic Sciences, 2016, 6 (3), 209–215.
[30] Moura, C. C.; Tare, R. S.; Oreffo, R. O. C.; Mahajan, S. Raman Spectroscopy and Coherent Anti-Stokes Raman Scattering Imaging: Prospective Tools for Monitoring Skeletal Cells and Skeletal Regeneration. Journal of The Royal Society Interface, 2016, 13 (118), 20160182.
[31] Guo, Z.; Zhang, Q.; Zou, H.; Guo, B.; Ni, J. A Method for the Analysis of Low-Mass Molecules by MALDI-TOF Mass Spectrometry. Anal. Chem., 2002, 74 (7), 1637–1641.
[32] Wang, P.; Giese, R. W. Recommendations for Quantitative Analysis of Small Molecules by Matrix-Assisted Laser Desorption Ionization Mass Spectrometry. J Chromatogr A, 2017, 1486, 35–41.
[33] Muyskens, M. A.; Glass, S. V.; Wietsma, T. W.; Gray, T. M. Data Acquisition in the Chemistry Laboratory Using LabVIEW Software. J. Chem. Educ., 1996, 73 (12), 1112.
[34] Drew, S. M. Integration of National Instruments’ LabVIEW Software into the Chemistry Curriculum. J. Chem. Educ., 1996, 73 (12), 1107.
[35] Belletti, A.; Borromei, R.; Ingletto, G. Teaching Physical Chemistry Experiments with a Computer Simulation by LabVIEW. J. Chem. Educ., 2006, 83 (9), 1353.
[36] Gostowski, R. Teaching Analytical Instrument Design with LabVIEW. J. Chem. Educ., 1996, 73 (12), 1103.
[37] Yang, M.; Xu, D.; Chen, S.; Li, H.; Shi, Z. Evaluation of Machine Learning Approaches to Predict Soil Organic Matter and pH Using Vis-NIR Spectra. Sensors, 2019, 19 (2), 263.
[38] El-Abassy, R. M.; Donfack, P.; Materny, A. Rapid Determination of Free Fatty Acid in Extra Virgin Olive Oil by Raman Spectroscopy and Multivariate Analysis. Journal of the American Oil Chemists’ Society, 2009, 86 (6), 507–511.
[39] Harris, C. R.; Millman, K. J.; van der Walt, S. J.; Gommers, R.; Virtanen, P.; Cournapeau, D.; Wieser, E.; Taylor, J.; Berg, S.; Smith, N. J.; et al. Array Programming with NumPy. Nature, 2020, 585 (7825), 357–362.
[40] Doménech-Carbó, M. T.; Doménech-Carbó, A. Spot Tests: Past and Present. ChemTexts, 2021, 8 (1), 4.
[41] Hsueh, S.-C.; Wang, L.-H.; Liao, Y.-C.; Chiang, H.-Y.; Lin, C.-H. Capillary Action-Driven Surface-Enhanced Raman Spectroscopy (SERS) for the Identification of Phthalocyanine Blue in Modern Paintings Based on the BPG Spot Test. Anal. Methods, 2024, 16 (14), 2147–2151.
[42] Zhang, Z.-M.; Liu, J.-F.; Liu, R.; Sun, J.-F.; Wei, G.-H. Thin Layer Chromatography Coupled with Surface-Enhanced Raman Scattering as a Facile Method for On-Site Quantitative Monitoring of Chemical Reactions. Anal. Chem., 2014, 86 (15), 7286–7292.
[43] Yao, H.; Dong, X.; Xiong, H.; Liu, J.; Zhou, J.; Ye, Y. Functional Cotton Fabric-Based TLC-SERS Matrix for Rapid and Sensitive Detection of Mixed Dyes. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2022, 280, 121464.
[44] Payne, T. D.; Dixon, L. R.; Schmidt, F. C.; Blakeslee, J. J.; Bennett, A. E.; Schultz, Z. D. Identification and Quantification of Pigments in Plant Leaves Using Thin Layer Chromatography-Raman Spectroscopy (TLC-Raman). Anal. Methods, 2024, 16 (16), 2449–2455.
[45] Nilghaz, A.; Mahdi Mousavi, S.; Amiri, A.; Tian, J.; Cao, R.; Wang, X. Surface-Enhanced Raman Spectroscopy Substrates for Food Safety and Quality Analysis. J. Agric. Food Chem., 2022, 70 (18), 5463–5476.
[46] Ngo, Y. H.; Li, D.; Simon, G. P.; Garnier, G. Gold Nanoparticle–Paper as a Three-Dimensional Surface Enhanced Raman Scattering Substrate. Langmuir, 2012, 28 (23), 8782–8790.
[47] Martins, N. C. T.; Fateixa, S.; Fernandes, T.; Nogueira, H. I. S.; Trindade, T. Inkjet Printing of Ag and Polystyrene Nanoparticle Emulsions for the One-Step Fabrication of Hydrophobic Paper-Based Surface-Enhanced Raman Scattering Substrates. ACS Appl. Nano Mater., 2021, 4 (5), 4484–4495.
[48] Sun, L.; Cao, C.; Zhi, Y.; Shan, Y.; Zhang, H.; Dou, B.; Zhang, L.; Huang, W. Au–Ag Nanoparticles with Controllable Morphologies for the Surface-Enhanced Raman Scattering Detection of Trace Thiram. ACS Appl. Nano Mater., 2023, 6 (6), 4253–4261.
[49] Chi, T. T. K.; Le, N. T.; Hien, B. T. T.; Trung, D. Q.; Liem, N. Q. Preparation of SERS Substrates for the Detection of Organic Molecules at Low Concentration. Communications in Physics, 2016, 26 (3), 261–261.
[50] Ju, Z.; Sun, J.; Liu, Y. Molecular Structures and Spectral Properties of Natural Indigo and Indirubin: Experimental and DFT Studies. Molecules, 2019, 24 (21), 3831.
[51] Amat, A.; Rosi, F.; Miliani, C.; Sgamellotti, A.; Fantacci, S. Theoretical and Experimental Investigation on the Spectroscopic Properties of Indigo Dye. Journal of Molecular Structure, 2011, 993 (1), 43–51.
[52] Nobre, D. C.; Delgado-Pinar, E.; Cunha, C.; Galvão, A. M.; Seixas de Melo, J. S. Indirubin: Nature Finding Efficient Light-Activated Protective Molecular Mechanisms. Dyes and Pigments, 2023, 212, 111116.
[53] Kim, N. Y.; Kim, Y.-C.; Kim, Y. G. Development of UHPLC-MS/MS Method for Indirubin-3′-Oxime Derivative as a Novel FLT3 Inhibitor and Pharmacokinetic Study in Rats. Molecules, 2020, 25 (9), 2039.
[54] Bonnett, R.; Czechowski, F.; Latos-Grazynski, L. Metalloporphyrins in Coal. 4. TLC-NMR of Iron Porphyrins from Coal: The Direct Characterization of Coal Hemes Using Paramagnetically Shifted Proton NMR Spectroscopy. Energy Fuels, 1990, 4 (6), 710–716.