Genome-inspired molecular identification in organic matter via Raman spectroscopy
Rapid, non-destructive characterization of molecular level chemistry for organic matter (OM) is experimentally challenging. Raman spectroscopy is one of the most widely used techniques for non-destructive chemical characterization, although it currently does not provide detailed identification of mo...
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| Format: | Article |
| Language: | en_US |
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Elsevier
2018
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| Online Access: | http://hdl.handle.net/1721.1/115091 https://orcid.org/0000-0003-1630-4052 https://orcid.org/0000-0003-4148-2424 https://orcid.org/0000-0003-1281-2359 |
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| author | Liu, Yun Ferralis, Nicola Bryndzia, L. Taras Grossman, Jeffrey C. |
| author2 | Massachusetts Institute of Technology. Department of Materials Science and Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Materials Science and Engineering Liu, Yun Ferralis, Nicola Bryndzia, L. Taras Grossman, Jeffrey C. |
| author_sort | Liu, Yun |
| collection | MIT |
| description | Rapid, non-destructive characterization of molecular level chemistry for organic matter (OM) is experimentally challenging. Raman spectroscopy is one of the most widely used techniques for non-destructive chemical characterization, although it currently does not provide detailed identification of molecular components in OM, due to the combination of diffraction-limited spatial resolution and poor applicability of peak-fitting algorithms. Here, we develop a genome-inspired collective molecular structure fingerprinting approach, which utilizes ab initio calculations and data mining techniques to extract molecular level chemistry from the Raman spectra of OM. We illustrate the power of such an approach by identifying representative molecular fingerprints in OM, for which the molecular chemistry is to date inaccessible using non-destructive characterization techniques. Chemical properties such as aromatic cluster size distribution and H/C ratio can now be quantified directly using the identified molecular fingerprints. Our approach will enable non-destructive identification of chemical signatures with their correlation to the preservation of biosignatures in OM, accurate detection and quantification of environmental contamination, as well as objective assessment of OM with respect to their chemical contents. |
| first_indexed | 2024-09-23T10:18:34Z |
| format | Article |
| id | mit-1721.1/115091 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T10:18:34Z |
| publishDate | 2018 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | mit-1721.1/1150912022-09-30T20:19:30Z Genome-inspired molecular identification in organic matter via Raman spectroscopy Liu, Yun Ferralis, Nicola Bryndzia, L. Taras Grossman, Jeffrey C. Massachusetts Institute of Technology. Department of Materials Science and Engineering Ferralis, Nicola Liu, Yun Ferralis, Nicola Grossman, Jeffrey C. Rapid, non-destructive characterization of molecular level chemistry for organic matter (OM) is experimentally challenging. Raman spectroscopy is one of the most widely used techniques for non-destructive chemical characterization, although it currently does not provide detailed identification of molecular components in OM, due to the combination of diffraction-limited spatial resolution and poor applicability of peak-fitting algorithms. Here, we develop a genome-inspired collective molecular structure fingerprinting approach, which utilizes ab initio calculations and data mining techniques to extract molecular level chemistry from the Raman spectra of OM. We illustrate the power of such an approach by identifying representative molecular fingerprints in OM, for which the molecular chemistry is to date inaccessible using non-destructive characterization techniques. Chemical properties such as aromatic cluster size distribution and H/C ratio can now be quantified directly using the identified molecular fingerprints. Our approach will enable non-destructive identification of chemical signatures with their correlation to the preservation of biosignatures in OM, accurate detection and quantification of environmental contamination, as well as objective assessment of OM with respect to their chemical contents. 2018-04-30T15:42:06Z 2018-04-30T15:42:06Z 2016-02 2016-02 Article http://purl.org/eprint/type/JournalArticle 0008-6223 http://hdl.handle.net/1721.1/115091 Liu, Yun, Nicola Ferralis, L. Taras Bryndzia, and Jeffrey C. Grossman. “Genome-Inspired Molecular Identification in Organic Matter via Raman Spectroscopy.” Carbon 101 (May 2016): 361–367 © 2016 Elsevier Ltd https://orcid.org/0000-0003-1630-4052 https://orcid.org/0000-0003-4148-2424 https://orcid.org/0000-0003-1281-2359 en_US http://dx.doi.org/10.1016/j.carbon.2016.02.017 Carbon Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Elsevier Nicola Ferralis |
| spellingShingle | Liu, Yun Ferralis, Nicola Bryndzia, L. Taras Grossman, Jeffrey C. Genome-inspired molecular identification in organic matter via Raman spectroscopy |
| title | Genome-inspired molecular identification in organic matter via Raman spectroscopy |
| title_full | Genome-inspired molecular identification in organic matter via Raman spectroscopy |
| title_fullStr | Genome-inspired molecular identification in organic matter via Raman spectroscopy |
| title_full_unstemmed | Genome-inspired molecular identification in organic matter via Raman spectroscopy |
| title_short | Genome-inspired molecular identification in organic matter via Raman spectroscopy |
| title_sort | genome inspired molecular identification in organic matter via raman spectroscopy |
| url | http://hdl.handle.net/1721.1/115091 https://orcid.org/0000-0003-1630-4052 https://orcid.org/0000-0003-4148-2424 https://orcid.org/0000-0003-1281-2359 |
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