@article{McCall:2025:10.1021/acssusresmgt.5c00104,
author = {McCall, MA and Watson, JS and Tan, JSW and Sephton, MA},
doi = {10.1021/acssusresmgt.5c00104},
journal = {ACS Sustainable Resource Management},
pages = {842--852},
title = {Biochar stability revealed by FTIR and machine learning},
url = {http://dx.doi.org/10.1021/acssusresmgt.5c00104},
volume = {2},
year = {2025}
}

Download

TY  - JOUR
AB  - 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.
AU  - McCall,MA
AU  - Watson,JS
AU  - Tan,JSW
AU  - Sephton,MA
DO  - 10.1021/acssusresmgt.5c00104
EP  - 852
PY  - 2025///
SN  - 2837-1445
SP  - 842
TI  - Biochar stability revealed by FTIR and machine learning
T2  - ACS Sustainable Resource Management
UR  - http://dx.doi.org/10.1021/acssusresmgt.5c00104
UR  - https://doi.org/10.1021/acssusresmgt.5c00104
VL  - 2
ER  - 

Download