Removal of aluminum from alkaline aqueous solution by adsorption on Degussa P25 TiO<sub>2</sub> and vermiculite concrete‐supported ferric oxyhydroxide
Bibliographic record
Abstract
Abstract The current investigation on aluminum adsorption from an aqueous solution originated from the need to implement a water treatment technology that would decrease the prohibitive aluminum concentrations in the groundwater of the Khibiny Alkaline Massif (Kola Peninsula), which is limiting the use of this water source as a source for the household water supply in the area. TiO2 and vermiculite concrete‐supported ferric oxyhydroxide (VC) were used as adsorbents to remove aluminum from the alkaline aqueous solution, considering the water‐specific natural conditions of the Khibiny aquifer. The effects of pH, initial aluminum concentration, contact time, adsorbent dose, and ions present in Khibiny groundwater were investigated. The results showed that the adsorption kinetics followed the pseudo‐second order model, and the equilibrium data were described well by Langmuir and Freundlich isotherm models. The aluminum adsorption on TiO2 exhibited a dramatically higher adsorption rate compared to VC; the adsorption equilibrium was reached in 30 and 240 minutes, respectively, while the maximum TiO2 and VC adsorption capacities were similar and had values of 6.85 and 6.75 mg g−1 as the results of laboratory tests revealed. It was shown that when these two adsorbents worked jointly the capacity reached 8.28 mg g−1 within 60 minutes at pH 9.0. No apparent significant effects on aluminum removal by adsorbents tested in the presence of SO42−, NO3−, and Cl− were observed. The obtained outcomes identify a possible approach and reliable foundation for water treatment technology to solve the problem of elevated aluminum concentrations in the household water of the Khibiny Alkaline Massif area as well as within other locations where the solution of this problem can improve living conditions or industrial technologies.
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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) | 0.001 | 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".