基于拉曼光谱成像技术的铁质文物锈蚀产物定量模型研究

铁质文物是我国文化遗产的重要组成部分。由于化学性质较为活泼, 铁质文物易发生腐蚀劣化。锈蚀产物对铁质文物的稳定性有较大影响, 因此判断铁质文物锈蚀产物的组成特征, 对于铁质文物稳定性评估具有重要意义。以赤铁矿（α-Fe2O3）, 磁铁矿（Fe3O4）, 四方纤铁矿（β-FeOOH）三种铁质文物的锈蚀产物为研究对象, 采用拉曼光谱成像结合主成分回归法（PCR）和偏最小二乘法（PLS）, 同时结合多种预处理方法, 构建了两组二元混合锈蚀（α-Fe2O3+Fe3O4, α-Fe2O3+β-FeOOH）的定量模型。结果表明, 对于α-Fe2O3+Fe3O4二元体系, PCR和PLS算法构建模型的定量效果基本一致, α-Fe2O3和Fe3O4的PLS定量模型结果均表明, 一阶导数+Savitsky-Golay（S-G）平滑（9）条件下建模效果最好。对于α-Fe2O3+β-FeOOH二元体系, PLS方法所构建模型优于PCR方法, α-Fe2O3和β-FeOOH的PLS定量模型结果均表明, MSC+S-G平滑（5）条件下建模效果最好。研究结果为定量评估铁质文物锈蚀产物的化学稳定性提供了有效方法。

Abstract

Iron artefacts are important part of the cultural heritage in China. Due to the high activity of iron, iron artefacts are prone to corrosion and deterioration. Corrosion products greatly influence the stability of iron cultural relics. Therefore, determining the composition of iron corrosion products is significant for evaluating iron artefacts’ stability. In this study, pure chemical reagents were used to simulate three types of corrosion products commonly found on iron artefacts, including hematite (α-Fe2O3), magnetite (Fe3O4), and akaganeite (β-FeOOH). Raman spectroscopic imaging, combined with Principal Components Regression (PCR), Partial Least Squares (PLS) and multiple spectral pretreatment methods, were applied to establish quantitative models for two sets of a binary mixture of standard corrosion samples (α-Fe2O3+ Fe3O4, α-Fe2O3+β-FeOOH). The results indicate that, for α-Fe2O3+Fe3O4 mixed standard samples, the model effects of PCR and PLS algorithms are not much different. The quantitative model results show that the best spectral processing method of PCR modeling is first derivative +Savitsky-Golay (S-G) smoothing (9). For α-Fe2O3+β-FeOOH mixed standard samples, the model constructed by the PLS method is superior to the PCR method. The best PLS modelling spectral processing method is MSC+S-G smoothing (5). The research results provide an effective method for quantitatively evaluating the chemical stability of corrosion products of iron artefacts.

参考文献

[1] Crampton D, Holloway K, Fraczek J, et al. Lowa. Dept. of Transportation Office of Bridges and Structures, 2013.

[2] Grousset S, Kergourlay F, Neff D, et al. Journal of Analytical Atomic Spectrometry, 2015, 30(3): 721.

[3] Dubois F, Mendibide C, Pagnier T, et al. Corrosion Science, 2008, 50(12): 3401.

[4] Veneranda M, Aramendia J, Bellot-Gurlet L, et al. Corrosion Science, 2018, 133: 68.

[5] LUO Rui, WU Jun, LIU Xin-long, et al(罗睿, 吴军, 柳鑫龙, 等). Journal of Chinese Society for Corrosion and Protection(中国腐蚀与防护学报), 2014, 34(6): 566.

[6] Monnier J, Dillmann P, Legrand L, et al. British Corrosion Journal, 2013, 45(5): 375.

[7] Monnier J, Bellot-Gurlet L, Baron D, et al. Journal of Raman Spectroscopy, 2011, 42(4): 773.

[8] Betancur A, Pérez F, Correa M, et al. Optica Puray Aplicada, 2012, 45(3): 269.

[9] Reig F, Adelantado J, Moreno M. Talanta, 2002, 58(4): 811.

[10] Shashoua Y, Degn Berthelsen M B, Nielsen O. Journal of Raman Spectroscopy, 2006, 37(10): 1221.

[11] Salpin F, Trivier F, Lecomte S, et al. Journal of Raman Spectroscopy, 2006, 37(12): 1403.

[12] Daher C, Pimenta V, Bellot-Gurlet L. Talanta, 2014, 129: 336.

[13] Hrlé S, Mazaudier F, Dillmann P, et al. Corrosion Science, 2004, 46(6): 1431.

[14] Aramendia J, Gomez Nubla L, Bellot Gurlet L, et al. Journal of Raman Spectroscopy, 2014, 45(11-12): 1076.

[15] Li Shengxi, Hihara L. Journal of The Electrochemical Society, 2015, 162(9): C495.

[16] Watkinson D, Emmerson N. Environmental Science and Pollution Research, 2017, 24(3): 2138.

[17] Das S, Hendry M. Chemical Geology, 2011, 290(3-4): 101.

[18] CHU Xiao-li（褚小立）. Molecular Spectroscopy Analytical Technology Combined With Chemometrics and Its Applications（化学计量学方法与分子光谱分析技术）. Beijing: Chemical Industry Press(北京: 化学工业出版社), 2011. 311.

[19] LIU Yan-de, JIN Tan-tan, WANG Hai-yang（刘燕德, 靳昙昙, 王海阳）. Optics and Precision Engineering(光学精密工程), 2015, 23（9）: 2490.

程枭翔, 吴娜, 刘薇, 王克青, 李辰元, 陈坤龙, 李延祥. 基于拉曼光谱成像技术的铁质文物锈蚀产物定量模型研究[J]. 光谱学与光谱分析, 2023, 43(7): 2166. CHENG Xiao-xiang, WU Na, LIU Wei, WANG Ke-qing, LI Chen-yuan, CHEN Kun-long, LI Yan-xiang. Research on Quantitative Model of Corrosion Products of Iron Artefacts Based on Raman Spectroscopic Imaging[J]. Spectroscopy and Spectral Analysis, 2023, 43(7): 2166.

基于拉曼光谱成像技术的铁质文物锈蚀产物定量模型研究

关于本站 Cookie 的使用提示

全站搜索

基于拉曼光谱成像技术的铁质文物锈蚀产物定量模型研究

相关论文

相关资讯

关于本站 Cookie 的使用提示

全站搜索