Adsorption characteristics and mechanism analysis of heavy metal Cu(II) by cement soil
Bibliographic record
Abstract
Cemented soil (CS), a specialised construction material, holds application value in controlling heavy metal ion migration and preventing environmental contamination. This study systematically investigated the adsorption behaviour and mechanisms of copper ion (Cu(II)) on CS under varying particle sizes and curing times through adsorption kinetics and isothermal adsorption experiments. The results demonstrated that the Elovich model (R2 > 0.94) exhibited lower error functions and more accurately described the heterogeneous surface diffusion reaction processes of CS. Chemisorption was the dominant mechanism. The isothermal adsorption behaviour evolved from the Freundlich model at the curing time of 14 days to the Sips model at the curing time of 28 days (approximating the Langmuir model), indicating a gradual transition from multilayer adsorption on heterogeneous surfaces to saturated monolayer adsorption. Notably, the equilibrium adsorption capacity of fine-grained CS (<0.075 mm) increased by 57.81% during the 7–28-day curing period, whereas coarse-grained CS [0.1–0.25 mm) exhibited only an 8.58% increase. This study identified a critical copper ion concentration threshold of 200–400 mg/l, marking the transition of CS from adsorption-dominated removal to a synergistic adsorption–precipitation mechanism. These quantitative findings provide a foundation for optimising CS-engineered barrier design and demonstrate value in heavy metal contamination 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) | 0.000 | 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".