Rapid discrimination of porous bio-carbon derived from nitrogen rich biomass using Raman spectroscopy and artificial intelligence methods

Kumbhar, D and Palliyarayil, A and Reghu, D and Shrungar, D and Umapathy, S and Sil, S (2021) Rapid discrimination of porous bio-carbon derived from nitrogen rich biomass using Raman spectroscopy and artificial intelligence methods. In: Carbon, 178 . pp. 792-802.

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Abstract

Granular porous bio-carbons were prepared by pyrolysis of different biomass precursors such as mung bean, black urad bean, and black grape seed, using ZnCl2 as an activating agent at different activation temperatures of 450�750 °C. The derived bio-carbons samples were extensively characterized using electron microscopy, surface area analysis, and CO2 adsorption capacity studies. As the activation temperature increased, the surface area, pore volume, nitrogen content, and the CO2 removal efficiency of the bio-carbons varied from 254 to 937 m2/g, 0.1241�0.4212 mL/g, 1.51�6.23, and 2.11�5.48 mmol/g (at 25 °C under 3 bar pressure) respectively. Furthermore, Raman spectroscopic technique was used as a tool to understand the structural development that occurred in the biomasses during pyrolysis. Additionally, multivariate analysis such as combined Principal Component Analysis, partial least square-discriminant analysis (PLS-DA) was employed for the Raman data to discriminate the biomass based on their source and activation temperature. In addition, deep learning methods such as LeNET, ResNet, CAE were evaluated to classify the bio-carbon samples with respect to temperature and precursor material. All the models gave 100 accuracy of classification with respect to the temperature of activation. An overall classification accuracy of >92 ± 0.0665 was obtained for LeNET model. © 2021 Elsevier Ltd

Item Type:	Journal Article
Publication:	Carbon
Publisher:	Elsevier Ltd
Additional Information:	The copyright for this article belongs to Elsevier Ltd
Keywords:	Biomass; Carbon; Chemical activation; Chlorine compounds; Deep learning; Discriminant analysis; Least squares approximations; Multivariant analysis; Nitrogen; Principal component analysis; Pyrolysis; Raman spectroscopy; Zinc chloride, Activating agents; Activation temperatures; Artificial intelligence methods; CO2 capture; Deep learning; Grape seeds; Mungbeans; Partial least square analysis; Porous bio-carbon; ZnCl-2, Carbon dioxide
Department/Centre:	Division of Chemical Sciences > Inorganic & Physical Chemistry Division of Physical & Mathematical Sciences > Instrumentation Appiled Physics
Date Deposited:	14 Jul 2021 11:46
Last Modified:	14 Jul 2021 11:46
URI:	http://eprints.iisc.ac.in/id/eprint/68778

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