Adsorptive Removal of Iron and Manganese from Groundwater Samples in Ghana by Zeolite Y Synthesized from Bauxite and Kaolin
Bibliographic record
Abstract
Ground water samples from residential homes in three Regions of Ghana: Central, Greater Accra and Ashanti, were analyzed for iron and manganese contamination. The samples were exposed to characterized zeolite Y by X-ray diffraction, scanning electron microscopy, energy dispersive X-ray spectroscopy, Fourier transformed-infrared spectroscopy and thermos gravimetric-differential thermal analysis. Zeolite Y is able to remove 98% of iron and 97% of manganese within an hour. The adsorption of both iron and manganese followed the Freundlich model, suggesting that the ions were transported onto the zeolite Y surface and subsequently diffused into the zeolite Y framework. The kinetic studies showed that pseudo-first order and intra particle and film diffusion models provided the best fit. The adsorption at 0.2 mg L−1 Fe ( Q 0.2 ) is calculated to be 0.023 mg g−1 for the Freundlich adsorption model, whilst that of manganese at 0.05 mg L−1 Mn ( Q 0.05 ) is evaluated to be 0.015 mg g−1. The zeolite retains its adsorption properties when retrieved from the first exposure water sample, washed copiously with distilled water and added to fresh water samples. The results suggest that zeolite Y can be used as a potential adsorbent for the removal of iron and manganese from groundwater.
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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".