Effect of the coexistence of SO32− and PO43− on the adsorption performance of zeolite-loaded FeOOH@ZnO for S2−
Bibliographic record
Abstract
Abstract The present study deals with the synthesis of zeolite-loaded FeOOH@ZnO by hydrothermal method and investigates the effects of coexisting SO32− and PO43− ions in the aqueous solution on the adsorption performance for S2−. The results showed that the HNO3-modified zeolite loaded with FeOOH@ZnO (FeOOH@ZnO/HZ) resulted in a maximum S2− removal rate of ≈98%. The adsorbent's performance on removing S2− was significantly enhanced, compared with NaOH and ZnCl2-modified zeolites loaded with FeOOH@ZnO, and the adsorption was proved to be a heat-absorbing process. When SO32− and PO43− coexisted with S2−, SO32− and PO43− had a significant influence on the adsorption properties of FeOOH@ZnO/HZ. When three ions of S2−, SO32− and PO43− were present simultaneously, the adsorption performance of FeOOH@ZnO/HZ on S2− was further, and the removal rate dropped to about 80%. Moreover, FeOOH@ZnO/HZ also adsorbed PO43− and SO32− in the system containing multiple ions, but the adsorption rates of PO43− and SO32− were much lower than S2−. This indicated that the adsorption of S2− in the presence of FeOOH@ZnO/HZ dominates under competitive conditions.
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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) | 0.001 | 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".