Biochar as a carrier for plant growth-promoting bacteria in phytoremediation of pesticides
Bibliographic record
Abstract
• Biochar enhances phytoremediation by supporting plant growth-promoting bacteria (PGPB). • The review explores biochar's properties and its role in pesticide degradation. • PGPB improve plant growth and stress tolerance through various mechanisms. • Future research is needed to address challenges in biochar and PGPB applications. This review examines the role of biochar as a carrier for plant growth-promoting bacteria (PGPB) in the phytoremediation process of pesticides. It begins by exploring the properties and performance of biochar, including its production processes, physical and chemical characteristics. The review then discusses the roles and mechanisms of PGPB, such as nitrogen fixation, phosphate solubilization, and phytohormone production, emphasizing how these bacteria can enhance plant growth and tolerance to environmental stresses while aiding in pesticide degradation. Biochar's suitability as a carrier for PGPB is highlighted due to its porous structure, surface chemistry, and ability to create microbial habitats. The interactions between biochar, PGPB, and plants that can enhance phytoremediation efficiency are examined. Additionally, the review identifies challenges and limitations, suggesting areas for further research to develop practical applications. This review aims to provide a comprehensive overview of biochar's potential as a carrier for PGPB in improving phytoremediation outcomes, explicitly addressing the lack of prior reviews on this topic and highlighting broader implications for sustainable remediation.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".