Exploring the arsenic removal potential of various biosorbents from water
Bibliographic record
Abstract
Globally, contamination of groundwater with toxic arsenic (As) is an environmental and public health issue given to its carcinogenic properties, thereby threatening millions of people relying on drinking As-contaminated well water. Here, we explored the efficiency of various biosorbents (egg shell, java plum seed, water chestnut shell, corn cob, tea waste and pomegranate peel) for arsenate (As(V)) and arsenite (As(III)) removal from As-contaminated water. Significantly, egg shell and java plum seed displayed the greatest As(III) elimination (78–87%) at 7 pH followed by water chestnut shell (75%), corn cob (67%), tea waste (74%) and pomegranate peel (65%). In contrast, 71% and 67% of As(V) was removed at pH 4.1 and 5.3 by egg shell and java plum seed, respectively. The maximum As(V) and As(III) sorption by all the biosorbents was obtained, notably for egg shell and java plum seed, after 2 h contact time. Langmuir isotherm and pseudo-second order models best fitted the sorption data for both forms of As. The –OH, –COOH, –NH2 and sulfur-bearing surface functional groups were possibly involved for As(III) and As(V) removal by biosorbents. The scanning electron microscopy combined with the energy dispersive X-ray spectroscopy (SEM-EDX) analysis showed that the heterogeneous surface of biosorbents, possessing rough and irregular areas, could have led to As sorption. Both As(V) and As(III) were successfully desorbed (up to 97%) from the biosorbents in four sorption/desorption (regeneration) cycles. This pilot-scale study highlights that egg shell and java plum seed have the greatest ability to remove both As species from As-contaminated drinking water. Importantly, these findings provide insights to develop an inexpensive, effective and sustainable filtration technology for the treatment of As in drinking water, particularly in developing countries like Pakistan.
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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) | 0.001 | 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".