Steel slag - corn straw biochar composite for reducing arsenic bioavailability in paddy soil: Effectiveness and mechanisms
Bibliographic record
Abstract
Arsenic (As) pollution poses a serious threat to both ecological systems and human health, making the development of economical and efficient remediation materials a key research priority. Steel slag (SS) and corn straw biochar (BC) are promising low-cost precursors with strong potential for As immobilization. In this study, steel slag–corn straw biochar composites (SSBCs) were produced via co-pyrolysis to elucidate their mechanisms for immobilizing As in contaminated paddy soils and inhibiting As uptake by ryegrass. Through ryegrass pot experiments and adsorption analyses, the As-reduction performance of BC, SS, and SSBCs was systematically evaluated, with particular emphasis on their effectiveness in lowering As bioavailability in paddy soil. Pot experiments revealed the application of 2 % SSBCs increased ryegrass biomass while markedly decreasing As accumulation in ryegrass roots. Furthermore, SSBC₁ (SS: BC= 1:1, mass ratio) and SSBC₂ (2.5:1) treatments elevated soil pH and significantly reduced available As concentrations. Characterization analyses indicated that the enhanced As(III) removal efficiency of SSBC₁ was primarily attributable to its hydroxyl, Ca–O, and Fe–O functional groups and its porous structure, which promoted surface complexation and electrostatic adsorption. In adsorption experiments, both BC and SSBCs rapidly adsorbed As(III), reaching equilibrium within 120 min. Among them, SSBC₁ exhibited the highest adsorption capacity (21.64 mg g⁻¹), representing a six-fold increase compared with BC (3.46 mg g⁻¹). Adsorption capacity increased under acidic conditions (pH 3.0–7.0), and SSBC₁ significantly enhancing soil As(III) retention. These mechanisms contributed to effective As immobilization, thereby reducing its bioavailability and subsequent plant uptake. Overall, the preparation of SSBCs improved the physicochemical properties and surface structure of BC, enhanced its adsorption performance, and demonstrated strong potential for the remediation of As-contaminated paddy 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".