Role of reactive oxygen species (ROS) on biochar enhanced chromium phytoremediation in the soil–plant system: Exploration on detoxification mechanism
Bibliographic record
Abstract
Biochar, as an amendment to enhance phytoremediation of heavy metal contamination, can mediate reactive oxygen species (ROS) generation. However, the role of biochar-mediated ROS (BMR) during soil-plant phytoremediation remains inadequately understood. In this study, a combination of pot experiments, chemical extraction, and partial least squares path modeling (PLS-PM) was employed to investigate BMR dynamics and their influence on chromium (Cr) accumulation and detoxification in plants. Biochar addition promoted Cr removal efficiency and decreased ROS concentrations in soil, notably reaching the largest removal efficiency of 80.60 % and the lowest ROS concentration of 37.53 μmol/kg in BC-3 group at 90d. Decreased ROS concentrations in soil facilitated the plant absorbing water-soluble Cr (VI), adsorbed Cr (VI), and chromate-precipitated Cr (VI) in soil, and enhanced Cr accumulation in metabolically inactive compartments (cell walls and vacuoles). When biochar was added at concentrations of 2 % and 3 % (w/w), ROS concentrations in plant tissues decreased to signaling molecule thresholds. This reduction further stimulated antioxidant enzyme activity, promoted the reduction of Cr (VI) within subcellular organelles, and enhanced Cr cell wall fixation and vacuolar compartmentation, ultimately achieving their synergistic integration with Cr detoxification with accumulation. This study provides an in-depth understanding of BMR-related mechanisms during phytoremediation and valuable insights into strategies for enhancing mitigation of variable valence heavy metals in soils.
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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) | 0.000 | 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 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".