Exploring the arsenic removal potential of various biosorbents from water
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.024 | 0.003 |
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 it