Mercury(II) Ion Adsorption Behavior in Thiol-Functionalized Mesoporous Silica Microspheres
Bibliographic record
Abstract
Microspherical mercury ion adsorbents with uniform mesopore channels were prepared by fluoride-catalyzed surfactant-directed co-condensation of tetraethoxysilane (TEOS) and 3-mercaptopropyltrimethoxysilane (MPTMS) using mildly acidic nonionic surfactant solutions. Using batch adsorption methods, mercury(II) adsorption isotherms and kinetic uptake profiles for these adsorbents were obtained. The mercury(II) isotherms of the microspheres with low thiol group loadings did not fit the Langmuir expression and were indicative of weak interactions between the mercury(II) ions and the adsorbents. With increasing thiol group loading, the isotherms approached type-I in appearance, indicating improved binding energetics of the ions with the adsorbents. The adsorption kinetics of the adsorbents suggested that the uptake of mercury ions by the microspheres was rather slow, with diffusion coefficients ranging between 10 - 14 and 10 - 15 m 2 s - 1 . The coefficients were found to increase as a function of the thiol group density of the adsorbents. The diffusion coefficients of the mercury(II) adsorption process were also found to increase as a function of time, indicating synergistic acceleration of the uptake rate with increasing mercury ion loading in the materials. On the basis of these observed trends, an ion permeation and displacement mechanism is proposed and described.
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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".