Sorption of Arsenite on Cu-Al, Mg-Al, Mg-Fe, and Zn-Al Layered Double Hydroxides in the Presence of Inorganic Anions Commonly Found in Aquatic Environments
Bibliographic record
Abstract
Use of layered double hydroxides (LDHs) in the environmental field is gaining popularity due to their potential to sorb toxic anions, attributed to their large surface area, high anion exchange capacity, and good thermal stability. In this study, four different LDHs (i.e., Cu-Al-, Mg-Al-, Mg-Fe-, and Zn-Al-LDH) were synthesized to select one or more efficient sorbents, capable of removing arsenite [As(III)] from contaminated waters. In particular, we studied the following: (1) X-ray diffraction patterns and specific surface area of the synthesized LDHs; (2) sorption isotherms of As(III) at pH 7.0; and (3) sorption of As(III) on LDHs, in the presence of inorganic anions [carbonate (CO3), chloride (Cl), fluoride (F), phosphate (PO4), sulfate (SO4)] commonly present in aquatic environments. The poorly crystalline LDHs (i.e., Cu-Al-LDH and Mg-Fe-LDH) sorbed greater amounts of As(III) than the well-crystalline LDHs (i.e, Mg-Al-LDH and Zn-Al-LDH). The efficiency of the competing anions at inhibiting As(III) sorption by the LDHs was Cl ≤ F < SO4 << CO3 << PO4, regardless of initial ligand/As(III) molar ratios (R) or LDH. Although Cu-Al-LDH sorbed lower amounts of As(III) than the Mg-Fe-LDH, it showed, surprisingly, a higher affinity for As(III). This surprising behavior puts this LDH in the forefront as a potential sorbent for the treatment of arsenic-contaminated waters.
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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".