Arsenic removal performance of granular adsorbents using novel clinoptilolites modified with iron nanoparticles
Bibliographic record
Abstract
Abstract In this research, novel beads were successfully synthesized from natural clinoptilolite (CL) modified with iron nanoparticles (FCL). Beads were prepared using alginate (A) and chitosan (C). FESEM, EDX, XRD, and FTIR techniques were employed to characterize the CL, FCL, A-bead, and C-bead. A comprehensive series of batch adsorption experiments were conducted utilizing both prepared samples to study the influential parameters. The most effective cross-linking solutions of chitosan and alginate were sodium tripolyphosphate/sodium hydroxide and 4% Ferric chloride, respectively. The optimum ratio of chitosan/FCL and alginate/FCL was 1:3 and 1:4, sequentially. The effect of initial arsenic concentration, and adsorbent concentration were evaluated. The optimal removal rate of 86% and 93.27% were found for A-beads and C-beads using their optimized initial concentration. Moreover, the best arsenic removal performance was seen 1 g/L for both A-beads and C-beads. The removal rate of 0.3, 0.6, 1, 1.5, and 2 g/L of alginate was 75.12%, 81.15%, 82.21%, 82.90%, and 83.15%, respectively. On the other hand, C-beads had higher removal rates at the considered contents. The removal rate of 0.3, 0.6, 1, 1.5, and 2 g/L of C-beads were 75.18%, 88.78%, 91.86%, 92.25%, and 92.4%, respectively. Additionally, Langmuir and Freundlich isotherms were employed to find the maximum adsorption capacity. The maximum adsorption capacity of qmax of alginate and chitosan beads was 10000 𝜇g/g.
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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".