A comparative evaluation of mercury(II) adsorption equilibrium and kinetics onto silica gel and sulfur‐functionalised silica gels adsorbents
Bibliographic record
Abstract
This paper presents a comparative evaluation of mercury(II) adsorption equilibrium and kinetics onto silica gel and sulfur‐functionalised silica gel adsorbents. The silica gel was synthesised using tetraethyl orthosilicate (TEOS) as a precursor, while the sulfur‐functionalised silica gel adsorbents were prepared through a co‐condensation method using TEOS as a precursor with bis(triethoxysilylpropyl)tetrasulfide (BTESPT) and 3‐mercaptopropyl trimethoxysilane (MPTMS) as sulfur ligands. The synthesised adsorbents were characterized by using SEM, FTIR, nitrogen adsorption/desorption (NAD), and EDX. A batch adsorption experiment was employed to evaluate the removal of Hg(II) by using the adsorbents. The study revealed that the Hg(II) adsorption capacity was found to be dependent on the initial Hg(II) concentration, agitation time, and pH of the solution. The Hg(II) adsorption capacities obtained for the SG‐TEOS, SG‐BTESPT and SG‐MPTMS were 40.95, 93.32 and 102.37 mg/g, respectively. The Hg(II) adsorption was well fitted to the Langmuir isotherm models (R2 > 0.935) and obeyed the pseudo‐second order kinetics model (R2 > 0.991). A further analysis of the kinetics suggested that the Hg(II) adsorption process was governed by both intra‐particle and external mass transfer processes, in which film diffusion was the rate‐limiting step.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.001 | 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".