Remoción de arsénico (V) utilizando zeolita natural: pruebas de columna de lecho fijo
Bibliographic record
Abstract
Water pollution with arsenic has received special attention due to its health implications for the population. Therefore, its removal from groundwater is of vital importance. The main objective of this work was to investigate the removal performance of dissolved arsenic in multi-ionic solutions using a low-cost mineral material and, in parallel, to be able to compare its sorption capacity from its nature as a chemically modified form. To do this, fixed bed columns packed with natural zeolites (ZN) and chemically modified (ZMQ) with 1 M H2SO4 were implemented. Sorption studies showed that chemical conditioning improved the maximum sorption capacity (qe), the breakthrough time, the exhaustion time and the effluent volume with a concentration equal to or less than 10 μg/L, correspondingly by 150, 45, 88 and 281 %, concerning the ZN. The breakthrough curves for the removal of As (V) were fitted with various mathematical models, being the Thomas nonlinear model the one that best reproduced the kinetics of sorption under the implemented operating conditions. The X-ray diffraction concluded that there is no structural change in the zeolite after the chemical modification, therefore, the increase in the sorption capacity of the ZMQ was attributed to the morphological and elemental chemical composition changes on their surface. These results show that the ZMQ can be used as a viable alternative, from the point of view of purifying efficiency, concerning its non-acidified form, for the removal of As (V).
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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 | 0.001 | 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".