Kinetics of Cd(<scp>ii</scp>) adsorption and desorption on ferrihydrite: experiments and modeling
Bibliographic record
Abstract
The kinetics of Cd(ii) adsorption/desorption on ferrihydrite is an important process affecting the fate, transport, and bioavailability of Cd(ii) in the environment, which was rarely systematically studied and understood at quantitative levels. In this work, a combination of stirred-flow kinetic experiments, batch adsorption equilibrium experiments, high-resolution transmission electron microscopy (HR-TEM), and mechanistic kinetic modeling were used to study the kinetic behaviors of Cd(ii) adsorption/desorption on ferrihydrite. HR-TEM images showed the open, loose, and sponge-like structure of ferrihydrite. The batch adsorption equilibrium experiments revealed that higher pH and initial metal concentration increased Cd(ii) adsorption on ferrihydrite. The stirred-flow kinetic results demonstrated the increased adsorption rate and capacity as a result of the increased pH, influent concentration, and ferrihydrite concentration. The mechanistic kinetic model successfully described the kinetic behaviors of Cd(ii) during the adsorption and desorption stages under various chemistry conditions. The model calculations showed that the adsorption rate coefficients varied as a function of solution chemistry, and the relative contributions of the weak and strong ferrihydrite sites for Cd(ii) binding varied with time at different pH and initial metal concentrations. Our model is able to quantitatively assess the contributions of each individual ferrihydrite binding site to the overall Cd(ii) adsorption/desorption kinetics. This study provided insights into the dynamic behavior of Cd(ii) and a predictive modeling tool for Cd(ii) adsorption/desorption kinetics when ferrihydrite is present, which may be helpful for the risk assessment and management of Cd contaminated sites.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".