Thiol-modified olive-stone biochar preparation for Hg(II) removal from aqueous solutions
Bibliographic record
Abstract
Mercury (Hg) is a heavy metal whose toxicity poses significant environmental and health risks. Utilising biochar prepared from biomass waste is a straightforward and effective method for removing mercury from water. This research centred on producing a thiol-functionalised biochar derived from olive-stone waste for the removal of mercury (II) from aqueous solutions. Characterisation analyses confirmed successful functionalisation. The biochar, despite having a limited specific surface area (4.14 m 2 /g) due to raw material nature and pyrolysis conditions, exhibited a notable ability for mercury (II) adsorption, primarily attributed to the thiol-modified surface. Adsorption was assessed using a 2 3 factorial design, with the variables being adsorption time, biochar dose and initial mercury (II) concentration in the solution. Biochar dose emerged as the most influential factor, followed by adsorption time and, lastly, initial mercury (II) concentration. The peak removal efficiency of the model stood at 98.19%. The kinetics aligned with the pseudo-first-order and intraparticle diffusion models, suggesting a surface adsorption mechanism coupled with pore diffusion. This work accentuates the potential of olive-derived biochar, when thiol enhanced, in treating aqueous systems contaminated with mercury (II).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".