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Updated: Sep 3, 2025

Characterization, Quantification and Compound-specific Isotopic Analysis of Pyrogenic Carbon Using Benzene Polycarboxylic Acids BPCA
Published on: May 16, 2016
Stable polycyclic aromatic carbon (SPAC) formation in wildfire chars and engineered biochars
Alexandra Howell1, Sophia Helmkamp1, Erica Belmont1
1Department of Mechanical Engineering, The University of Wyoming, Laramie, WY, USA.
Stable polycyclic aromatic carbon (SPAC) in biochars is crucial for carbon models. This study links SPAC content to mass loss and elemental ratios, enabling prediction without specialized methods for wildfire and engineered chars.
Area of Science:
- Environmental Science
- Carbon Cycling
- Biogeochemistry
Background:
- Pyrogenic carbon (PyC), particularly stable polycyclic aromatic carbon (SPAC), is a long-term carbon sink.
- Accurate quantification of SPAC is vital for global carbon cycle models.
- Current SPAC characterization methods are often specialized and may not reflect real-world formation conditions.
Purpose of the Study:
- To investigate the relationship between SPAC formation and easily measurable physicochemical metrics.
- To develop predictive models for SPAC content based on mass loss and elemental ratios.
- To compare SPAC content in wildfire chars versus engineered biochars.
Main Methods:
- Utilized hydrogen pyrolysis (HyPy) to accurately measure SPAC content.
- Correlated SPAC content with mass loss and molar H:C and O:C ratios.
- Analyzed chars from both wildfires and engineered biochar production.
Main Results:
- SPAC formation showed a linear correlation with increased mass loss, indicating higher pyrolysis severity.
- Wildfire chars contained <30 wt% SPAC, suggesting a large labile fraction.
- Engineered biochars exhibited a range of SPAC content, up to approximately 75 wt%.
Conclusions:
- Physicochemical metrics (mass loss, elemental ratios) can reliably predict SPAC content.
- Developed predictive models can enhance accuracy in global carbon accounting.
- The study provides a more accessible method for characterizing SPAC in diverse char types.
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