Adsorption of Cadmium on Degraded Soils Amended with Maize-Stalk-Derived Biochar
Bibliographic record
Abstract
Biochar has been extensively proven to distinctively enhance the sorption capacity of both heavy metal and organic pollutants and reduce the related environmental risks. Soil pollution and degradation widely coexist, and the effect of biochar addition on adsorption behavior by degraded soils is not well understood. Four degraded soils with different degrees of degradation were amended with maize-stalk-derived biochar to investigate the adsorption of cadmium using batch methods. The maximum adsorption capacity (Qm) of degraded soil remarkably decreased in comparison with undegraded soil (5361 mg·kg−1→170 mg·kg−1), and the Qm of biochar increased with increasing pyrolysis temperature (22987 mg·kg−1→49016 mg·kg−1) which was much higher than that of soil. The addition of biochar can effectively improve the cadmium adsorption capacity of degraded soil (36–328%). The improving effect is stronger when increasing either the degradation level or the amount of added biochar, or the pyrolysis temperature of biochar. Contrary to the general soil–biochar system, adsorption of Cd was not enhanced but slightly suppressed (7.1–36.6%) when biochar was incorporated with degraded soils, and the adsorptivity attenuation degree was found to be negatively linear with SOM content in the degraded soil–biochar system. The results of the present study suggest that more attention on the adsorption inhibition and acceleration effect difference between the soil–biochar system and the degraded soil–biochar system is needed.
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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 | 0.000 | 0.001 |
| 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".