Deep-UV Raman spectroscopy: A novel heuristic method to characterize volcanologically relevant glasses on Mars

被引:5
|
作者
Cassetta, Michele [1 ,2 ]
Rossi, Barbara [3 ]
Mazzocato, Sara [4 ]
Vetere, Francesco [5 ]
Iezzi, Gianluca [6 ,7 ]
Pisello, Alessandro [8 ]
Zanatta, Marco [9 ]
Daldosso, Nicola [2 ]
Giarola, Marco [10 ]
Mariotto, Gino [2 ]
机构
[1] Univ Trento, Dept Ind Engn, I-38122 Trento, Italy
[2] Univ Verona, Dept Engn Innovat Med, I-37134 Verona, Italy
[3] Elettra Sincrotrone Trieste SCpA, I-34149 Trieste, Italy
[4] Univ Verona, Dept Comp Sci, I-37134 Verona, Italy
[5] Univ Siena, Dept Phys Sci Earth & Environm, I-53100 Siena, Italy
[6] Univ G dAnnunzio, Dept Engn & Geol, I-66100 Chieti, Italy
[7] Ist Nazl Geofis & Vulcanol INGV, I-00143 Rome, Italy
[8] Univ Perugia, Dept Phys & Geol, I-06100 Perugia, Italy
[9] Univ Trento, Dept Phys, I-38123 Trento, Italy
[10] Univ Verona, Ctr Technol Platform CPT, I-37134 Verona, Italy
关键词
Deep-ultraviolet Raman spectroscopy; Volcanic glass; Mars; ALUMINOSILICATE GLASSES; PLANETARY EXPLORATION; SILICATE MELTS; SODIUM; SPECIATION; VISCOSITY; RHEOLOGY; BEHAVIOR; H2O;
D O I
10.1016/j.chemgeo.2023.121867
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
Deep ultraviolet Raman spectroscopy is an essential component of the Perseverance rover operating on Mars. Here we propose a proof of concept of deep-UV Raman structural characterization of volcanologically relevant silicate glasses to provide a suitable analytical method of UV Raman spectra collected on Mars. The results show few but substantial spectral differences concerning those obtained by conventional Raman scattering using visible light laser sources. The evolution of the UV Raman spectra between 825 and 1300 cm-1 is confirmed to be more sensitive to the silica network's short-range structure than that below 700 cm-1 when compared to their visible counterpart. We adopted a Gaussian fitting model to parametrize the number of bridging oxygens resulting from the tetrahedral-oxygens stretching vibrations along a sub-alkaline join of volcanic glasses. We used these parameters to empirically provide a relation with the glass silica content. The model was externally validated with glasses having different and known compositions (in particular different amounts of iron, alkali and alumina) extending the validation set also to other magmatic series (i.e. alkaline and shoshonitic). Our approach enables a fast screening of deep-UV Raman spectra collected on Mars to disentangle the glass structure and retrieve its silica content within a given magmatic series. This method is relevant for planetary exploration and applies both to microanalysis of dry or hydrate volcanologically relevant glasses and general technical glasses since deep-UV Raman spectroscopy is particularly effective in reducing the unwished photoluminescence and increasing the signal-to-noise ratio.
引用
收藏
页数:10
相关论文
共 30 条
  • [21] Aluminum-black silicon plasmonic nano-eggs structure for deep-UV surface-enhanced resonance Raman spectroscopy
    Lin, Bo-Wei
    Tai, Yi-Hsin
    Lee, Yang-Chun
    Xing, Di
    Lin, Hsin-Chang
    Yamahara, Hiroyasu
    Ho, Ya-Lun
    Tabata, Hitoshi
    Daiguji, Hirofumi
    Delaunay, Jean-Jacques
    APPLIED PHYSICS LETTERS, 2022, 120 (05)
  • [22] Towards deep-UV surface-enhanced resonance Raman spectroscopy of explosives: ultrasensitive, real-time and reproducible detection of TNT
    Jha, Shankar K.
    Ekinci, Yasin
    Agio, Mario
    Loeffler, Joerg F.
    ANALYST, 2015, 140 (16) : 5671 - 5677
  • [23] A deep learning model designed for Raman spectroscopy with a novel hyperparameter optimization method
    Sui, An
    Deng, Yinhui
    Wang, Yuanyuan
    Yu, Jinhua
    SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY, 2022, 280
  • [24] Raman spectroscopy and FTIR spectroscopy fusion technology combined with deep learning: A novel cancer prediction method
    Leng, Hongyong
    Chen, Cheng
    Chen, Chen
    Chen, Fangfang
    Du, Zijun
    Chen, Jiajia
    Yang, Bo
    Zuo, Enguang
    Xiao, Meng
    Lv, Xiaoyi
    Liu, Pei
    SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY, 2023, 285
  • [25] ConInceDeep: A novel deep learning method for component identification of mixture based on Raman spectroscopy
    Zhao, Ziyan
    Liu, Zhenfang
    Ji, Mingqiang
    Zhao, Xin
    Zhu, Qibing
    Huang, Min
    CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2023, 234
  • [26] Routes to enabling Raman detection of in-theatre biological contaminants over extended range: Spatial Heterodyne Spectroscopy, Time-resolved Raman measurements, and the march towards the deep-UV
    Stothard, David J. M.
    Warden, Matthew S.
    Foster, Michael
    Brooks, William
    CHEMICAL, BIOLOGICAL, RADIOLOGICAL, NUCLEAR, AND EXPLOSIVES (CBRNE) SENSING XXII, 2021, 11749
  • [27] Observation of persistent -helical content and discrete types of backbone disorder during a molten globule to ordered peptide transition via deep-UV resonance Raman spectroscopy
    Brown, Mia C.
    Mutter, Andrew C.
    Koder, Ronald L.
    JiJi, Renee D.
    Cooley, Jason W.
    JOURNAL OF RAMAN SPECTROSCOPY, 2013, 44 (07) : 957 - 962
  • [28] A novel method for rice identification: Coupling Raman spectroscopy with Fourier spectrum and analyzing with deep learning
    Chai, Mengda
    Hasi, Wuliji
    Ming, Xiya
    Han, Siqingaowa
    Fang, Guoqiang
    Bu, Yingaridi
    JOURNAL OF FOOD COMPOSITION AND ANALYSIS, 2024, 136
  • [29] Investigations on the Novel Antimalarial Ferroquine in Biomimetic Solutions Using Deep UV Resonance Raman Spectroscopy and Density Functional Theory
    Domes, Robert
    Frosch, Torsten
    ANALYTICAL CHEMISTRY, 2023, 95 (19) : 7630 - 7639
  • [30] Application of deep UV resonance Raman spectroscopy to column liquid chromatography: Development of a low-flow method for the identification of active pharmaceutical ingredients
    Siegmund, Philipp
    Klinken, Stefan
    Hacker, Michael C.
    Breitkreutz, Joerg
    Fischer, Bjoern
    TALANTA, 2024, 277