Effectively uptake arsenate from water by mesoporous sulphated zirconia: Characterization, adsorption, desorption, and uptake mechanism
Bibliographic record
Abstract
Abstract Mesoporous sulphated zirconia (MSZ), prepared by a facile one‐step route, was characterized and served as arsenate adsorbent. The properties of MSZ were characterized by FTIR, N2 adsorption‐desorption isotherm, XRD, and TEM. It was found that SO42− was successfully incorporated into the obtained mesoporous material. Additionally, arsenate adsorption performance was executed by batch experiments. From the results, it was found that adsorption equilibrium data were fitted well to Langmuir‐Freundlich, and the maximum adsorption capacity was 99.23 mg/g at room temperature. The adsorption process obeyed pseudo‐second‐order under the investigated temperature, which indicated that “surface reaction” was the main rate‐limiting step. The uptake performance was not influenced by initial pH for pH in the region of 2.0–10.0. Based on the results of FTIR and the value of adsorption energy, it was demonstrated that ion‐exchange between arsenate species and sulphated groups was the dominant uptake mechanism. On this theory of uptake mechanism, 1.0 mol/L H2SO4 was successfully used to regenerate the spent MSZ. Arsenate removal percentage was still over 80 % after recycling the MSZ 3 times. These results indicated that MSZ possessed a potential application in treating arsenate‐contaminated water.
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 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".