Inorganic characterization of switchgrass biomass using laser-induced breakdown spectroscopy

Journal Article · Sun Nov 07 00:00:00 EDT 2021 · Spectrochimica Acta. Part B, Atomic Spectroscopy

DOI:https://doi.org/10.1016/j.sab.2021.106323· OSTI ID:1845791

Martin, Madhavi ^[1]; Brice, Deanne ^[1]; Martin, Samir ^[1]; André, Nicolas ^[2]; Labbé, Nikki ^[2]

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1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
2. Univ. of Tennessee, Knoxville, TN (United States). Center for Renewable Carbon

The inorganic characterization of 74 samples of switchgrass using laser-induced breakdown spectroscopy (LIBS) was undertaken. Determination of ash and inorganic elements content in biomass materials is vital for feedstock screening for bioconversion processes. Hierarchical models using principal component analysis (PCA) and partial least square analysis (PLS) were used to determine the presence of specific elemental micronutrients that are important in determining plant health for robust biomass production. LIBS uses a 532 nm laser with 45 mJ of laser power to excite the samples of switchgrass plant material and the emission of all the elements present in the plant samples were recorded in single spectra with a wide wavelength range of 200–800 nm. The results were compared to the laboratory standard technique, e.g., ICP-OES technique, to determine the true values for major micronutrients such as, silicon (Si), potassium (K), calcium (Ca), magnesium (Mg), phosphorus (P), and sulfur (S). Overall, our objectives were: 1) To determine the spectral features of switchgrass containing different amounts of these elements and 2) To examine the viability of this technique for determining the quality of the feedstock in terms of its inorganic composition. Cross-validation results showed that the broad-based model developed is promising for inorganics prediction in switchgrass. The LIBS validation prediction for the micronutrient elements mentioned here have been obtained. The regression coefficients for Si, were obtained to be 0.995, 0.994 for calibration and validation respectively, in case of Ca the regression coefficients were, 0.994 and 0.992 for calibration and validation. Similarly, in the case of Mg and K these were calculated to be 0.992 and 0.985, and 0.994 and 0.993 respectively. The regression coefficients are not as good as those for the elements mentioned, in case of the two elements S and P. They are 0.957, and 0.878, and 0.952 and 0.894 respectively for calibration, validation for the two elements. This demonstrates that LIBS-based techniques are inherently well suited for diverse environmental applications. Furthermore, LIBS along with PLS model can show capability in determining the viability of switchgrass as a biomass in the production of biofuels and survivability of switchgrass in processes associated with climate change. LIBS can help determining which switchgrass would be appropriate for a specific conversion process that favors low ash content overall or low value of specific inorganics.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Biological and Environmental Research (BER)
• Grant/Contract Number: AC05-00OR22725
• OSTI ID: 1845791
• Journal Information: Spectrochimica Acta. Part B, Atomic Spectroscopy, Journal Name: Spectrochimica Acta. Part B, Atomic Spectroscopy Journal Issue: 1 Vol. 186; ISSN 0584-8547
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

59 BASIC BIOLOGICAL SCIENCES
Elemental analysis
LIBS
Multivariate analysis
PCA
PLS
Switchgrass