Kinetics of Hg adsorption onto noncrystalline Al hydroxide as influenced by low-molecular-weight organic ligands
Bibliographic record
Abstract
This study was conducted to investigate the kinetics of Hg adsorption by noncrystalline Al hydroxide as influenced by various pH conditions and cysteine (cys), glycine (gly), and citric acid (cit), which have different structures and functionalities, as low-molecular-weight organic ligands using the GEOCHEM-PC software. The influence of these organic ligands on the kinetics of Hg adsorption varied according to their concentration, structure, and functionality and pH. The adsorption of Hg followed multiple first order kinetics with initial rapid adsorption, followed by slow adsorption. Cysteine suppressed or enhanced Hg adsorption, depending on pH and its concentration. Glycine and citric acid exerted suppressing and enhancing effects, respectively, with the exception of at pH 4.5 and at lower concentrations, at which no influence was observed as compared to the control. Two mechanisms were thought to mediate the adsorption of Hg(II); specific surface complexation in the control, cysteine and glycine systems, and ligand exchange in the citric acid system. The Hg adsorption at all levels of organic ligands decreased with increasing pH, with the exception of at the higher concentration of cysteine, at which the reverse trend was observed. The influence of organic ligands on the dynamics of Hg in the freshwater environment merits further study.
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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".