Biochar stability revealed by FTIR and machine learning
File(s)
Author(s)
McCall, Monica A
Watson, Jonathan S
Tan, Jonathan SW
Sephton, Mark A
Type
Journal Article
Abstract
Biochar is a carbon-rich and environmentally recalcitrant material, with strong potential for climate change mitigation. There is a need for rapid and accessible estimations of biochar stability, the resistance to biotic and abiotic degradation in soil. This study builds on previous work by integrating Fourier-transform infrared spectroscopy (FTIR) data with predictive modeling to estimate standard stability indicators: H:C and O:C molar ratios. Lignocellulosic feedstocks were pyrolyzed at highest treatment temperatures (HTT) ranging from 150–700 °C, and all samples achieved H:C < 0.7 and O:C < 0.4 at HTT of 400 °C and above. Several statistical and machine learning models were developed using FTIR spectra. The random forest (RF) models, which incorporated full data preprocessing, yielded the highest accuracy (R2 = 0.96 for both ratios) when tested on an unseen feedstock. Variable importance analysis identified spectral regions linked to aromaticity and inversely correlated to C–O stretches in cellulose and lignin as key predictors. The findings of this study verify that FTIR data can serve as a rapid and accurate tool for estimating biochar stability.
Date Issued
2025-05-22
Date Acceptance
2025-04-11
Citation
ACS Sustainable Resource Management, 2025, 2 (5), pp.842-852
ISSN
2837-1445
Publisher
American Chemical Society (ACS)
Start Page
842
End Page
852
Journal / Book Title
ACS Sustainable Resource Management
Volume
2
Issue
5
Copyright Statement
Copyright © 2025 The Authors. Published by American Chemical Society. This publication is licensed under CC-BY 4.0 (https://creativecommons.org/licenses/by/4.0/)
License URL
Identifier
10.1021/acssusresmgt.5c00104
Subjects
infrared spectroscopy
H:C
O:C
molar ratios
modeling
wood
grass
Random Forest
Publication Status
Published
Date Publish Online
2025-04-29