Removal of nickel ions on residue of alginate extraction from <i>Sargassum <scp>f</scp>ilipendula</i> seaweed in packed bed
Bibliographic record
Abstract
ABSTRACT The residue of the alginate extraction, which has been shown as a good alternative material in the removal of toxic metals from industrial wastewater, is little explored as a biosorbent material. This study evaluated the removal of nickel ion in a fixed bed onto the residue of alginate extraction from Sargassum filipendula seaweed. The biosorption process in a dynamic fixed‐bed system evaluated the influence of flow rate and feed concentration, by mass transfer zone (MTZ) and the total removal percentage (%Remt). In order to assess the metal recovery potential and the lifetime of the column, two cycles of adsorption/desorption were performed. The continuous adsorption process was simulated using different dynamic models such as Bohart and Adams, Clark, Thomas, Yan et al., and Yoon and Nelson models. The best predictive model was Yan et al. Techniques, such as Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy coupled with energy dispersive X‐ray (SEM‐EDX), helium gas picnometry, mercury porosimetry, and N2 physisorption (BET) were performed in order to compare the residue before adsorption with the material after the process. The results showed that the residue can be used to treat toxic metal contaminated effluents by biosorption processes efficiently.
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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".