Differences in the Physicochemical Properties of Wildfire Generated Pyrogenic Carbon and Biochar
Bibliographic record
Abstract
Pyrogenic carbon (PyC) results from the pyrolysis of organic materials through thermal decomposition at high temperatures in low oxygen environments (I.B.I. 2012). The broad term includes many forms of thermochemically altered carbon, including charcoal, black carbon, soot, and biochar (Scott et al. 2014), and consists of a pyrolyzed carbon fraction as well as an inorganic ash or mineral fraction. PyC is produced naturally during forest fires, where it forms at potentially high temperatures (up to 1200 °C) for very short periods of time (seconds to minutes for temperatures >300 °C; Santin et al. 2016a). Wildfire derived PyC has been shown to be a significant component of the carbon cycle, with an estimated 32 Tg of PyC cycled through aquatic environments annually (Santin et al. 2016b). Man-made biochar is generated under controlled conditions via pyrolysis in furnaces at controlled temperatures and under anoxic conditions (Ahmad et al. 2014), typically up to 700 °C, for longer periods of time (up to ~6 hours). Several studies have investigated the surface chemistry of biochar and its ability to remove metals from aqueous solution (e.g., Alam et al. (2018a), Alam et al. (2018b)). However, PyC produced during natural pyrogenic activity such as wild fires, is produced under highly variable temperatures and atmospheric conditions, in the presence of numerous and variable microenvironments which are challenging to measure (Scott et al. 2014), and its surface chemistry and reactivity is not well understood. To fill this gap, we investigate the physicochemical properties including the proton and metal adsorption potential of wildfire generated PyC (WF-PyC) collected from 4 locations within a recent forest fire along the Western slope of Mount Hunter, near Golden, British Columbia. We explored the binding capacity of a model cation (species of Cd 2+ ) under a range of environmentally relavent pH conditions (3-9) and then compared the findings to the adsorption potential of synthetically generated biochar produced from the same biomass. Fourier transform infrared (FTIR) and Raman spectroscopy was used to constrain the number and types of surface functional groups, and the coordination environment of Cd 2+ ions bound to WF-PyC and biochar. Potentiometric titrations were performed and modelled to calculate the acidity constants associated with each site and the total reactive surface area of both biochar and WF-PyC. Our results demonstrate greater reactivity to Cd 2+ associated with WF-PyC, not replicated in synthetic biochar of an equivalent biomass (Fig. 1). This both provides insight to the potential of WF-PyC to play a critical role as a vector for elemental transport in natural systems and also makes apparent the need to understand the pyrolysis conditions during forest fires to improve our understanding of its role in global metals transport and cycling.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".