Bibliographic record
Abstract
Rice husk adsorption column method has proved to be a promising solution for arsenic (As) removal over the other conventional methods. The present work investigates the potential of raw rice husk as an adsorbent for the removal of arsenic [As(V)] from drinking water. Effects of various operating parameters such as diameter of column, bed height, flow rate, initial arsenic feed concentration and particle size were investigated using continuous fixed bed column to check the removal efficiency of arsenic. This method shows maximum removal of As, i.e., 90.7 % under the following conditions: rice husk amount 42.5 g; 7 mL/min flow rate in 5 cm diameter column at the bed height of 28 cm for 15 ppb inlet feed concentration. Removal efficiency was increased from 83.4 to 90.7 % by reducing the particle size from 1.18 mm to 710 µm for 15 ppb concentration. Langmuir and Freundlich isotherm models were employed to discuss the adsorption behavior. The effect of different operating parameters on the column adsorption was determined using breakthrough curves. In the present study, three kinetic models Adam-Bohart, Thomas and Yoon–Nelson were applied to find out the saturated concentration, fixed bed adsorption capacity and time required for 50 % adsorbate breakthrough, respectively. At the end, solidification was done for disposal of rice husk.
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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".