New trends in biochar pyrolysis and modification strategies: feedstock, pyrolysis conditions, sustainability concerns and implications for soil amendment
Bibliographic record
Abstract
Abstract As a waste‐derived soil amendment with a long history, biochar has received extensive attention for its capability to improve soil fertility/health; remove or immobilize contaminants in soil, water and air; and mitigate climate change. With the aim of producing engineered biochars with excellent performances, new trends in biochar pyrolytic production and modification strategies have emerged. This review critically summarizes novel pyrolysis methods (e.g., microwave‐assisted pyrolysis, co‐pyrolysis and wet pyrolysis) and modification approaches (e.g., mineral modification, photocatalytic modification, electrochemical modification) with a focus on (a) the mechanisms involved in environmental remediation processes including soil immobilization, contaminant adsorption and catalytic oxidation; (b) effects of feedstock and pyrolysis conditions on physicochemical properties; (c) sustainability considerations in novel modification and pyrolysis strategies; and (d) the feasibility of extrapolating the results from wastewater treatment to soil remediation. It is argued that in order to achieve the maximum net environmental benefits, ‘greener’ modification methods are warranted, and the risks associated with pyrolysis of contaminated feedstock in soil amendment and contaminant sorption can be minimized through various novel approaches (e.g., co‐pyrolysis). Furthermore, novel pyrolysis methods can be combined with emerging modification strategies to synthesize more ‘effective’ biochars. Considering the similar aims of modification (e.g., increase surface area, introduce oxygen‐containing functional groups, increase aromaticity), the applicability of several novel approaches could in future can be expanded from contaminant adsorption/degradation in aqueous media to soil remediation/fertility improvement.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".