The role of clay minerals and the effect of H<sup>+</sup> ions on removal of heavy metal (Pb<sup>2+</sup>) from contaminated soils
Bibliographic record
Abstract
The importance of the surface charge of clay minerals (fixed or variable) and the effect of H+ ions on the adsorption and removal of Pb2+ ions from contaminated soil are investigated using kaolinite (variable charge) and two illitic (fixed charge) soils with pH 3.9 and 9.2. The adsorption-desorption characteristics of Pb2+ ions were determined using batch equilibrium tests and acid leach tests with various acids used to leach the soils. Under the same adsorption conditions, illitic soil adsorbed much more Pb2+ ions than kaolinite. The difference is largely due to the surface charges on the clay minerals. Removal of Pb2+ ions from variable-charge minerals (e.g., kaolinite) requires much less effort than removal of Pb2+ ions from constant-charge minerals (e.g., illite). The surface charge of a clay mineral has an important effect. By increasing the number of H+ ions available in the soil system with a buffer solution such as NaOAc-HOAc, heavy metals adsorbed on the clay surface are expelled to pore water. The increase in H+ ions in the soil system also assists in dissolving any metal carbonates, thereby increasing the solubility of heavy metals in illitic soil. The more H+ ions available in the pore fluid, the more Pb2+ ions can be released from the system.Key words: clay minerals, sorption, desorption, heavy metal, hydrogen ion, electrokinetic, acid leach.
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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".