Production and modifications of biochar to engineered materials and its application for environmental sustainability: a review
Bibliographic record
Abstract
Abstract Biochar, a carbon-rich material produced from biomass waste through thermal conversion, holds great environmental promise. This article offers a comprehensive overview of the various feedstocks used in biochar production, the different types of thermal degradation processes, biochar characterization, properties, modifications to engineered materials, and their applications in the environment. The quality of biochar, including surface area, pore size and volume, and functional group formation, is significantly influenced by the specific conditions under which thermal conversion takes place. Each of the diverse processes employed to produce biochar yields a distinct set of properties in the final product. In recent years, biochar has gained widespread recognition and utilization in diverse fields such as wastewater treatment, carbon sequestration, reduction of greenhouse gas emissions, biogas production, catalysis in biofuel industries, construction, and soil enhancement. In summary, biochar is a promising environmental mitigation tool to achieve a sustainable environment. In addition to its benefits, the application of biochar presents several challenges, including the selection of feedstocks, methods of biochar production, modifications to biochar, the properties of biochar, and the specific applications of biochar. The current review summarizes factors that could lead to significant advancements in future applications. Graphical Abstract
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".