Biochar composites: Emerging trends, field successes and sustainability implications
Bibliographic record
Abstract
Abstract Engineered biochars are promising candidates in a wide range of environmental applications, including soil fertility improvement, contaminant immobilization, wastewater treatment and in situ carbon sequestration. This review provides a systematic classification of these novel biochar composites and identifies the promising future trends in composite research and application. It is proposed that metals, minerals, layered double hydroxides, carbonaceous nanomaterials and microorganisms enhance the performances of biochars via distinct mechanisms. In this review, four novel trends are identified and assessed critically. Firstly, facile synthesis methods, in particular ball milling and co‐pyrolysis, have emerged as popular composite fabrication strategies that are suitable for large‐scale applications. Secondly, biochar modification with green materials, such as natural clay minerals and microorganisms, align well with the on‐going green and sustainable remediation (GSR) movement. Furthermore, new applications in soil health improvement and climate change mitigation support the realization of United Nation's Sustainable Development Goals (SDGs). Finally, the importance of field studies is getting more attention, since evidence of field success is critically needed before large‐scale applications.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".