Assessment of Moringa-functionalized carbon based biofilter for disinfection through column experiments
Why this work is in the frame
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Bibliographic record
Abstract
The lack of infrastructure for water treatment and distribution remains a major problem in many low-income regions across the globe. Many available water treatment technologies may not be successfully implemented due to economic constraints and low social acceptability. In this study, we test the extent to which Moringa oleifera (MO)-functionalized carbon biofilter columns can effectively remove bacterial contamination in water. MO proteins were adsorbed onto two carbon adsorbents, granular activated carbon (GAC) and rice husk ash (RHA), and were then used as packing materials for a biofilter column. Synthetic contaminated water (non-pathogenic E. coli in water) was fed at the top of the column at fixed flow rates, and coliform removal in the column was evaluated by monitoring the coliform breakthrough in the filtered water. A semi-factorial experimental design was adopted to evaluate the influence of column bed height, type of adsorbent, and contact time on the E. coli removal efficiencies. As a control, parallel experiments using bare carbon adsorbents were also performed. The effectiveness of MO-functionalized adsorbents was evaluated through ANOVA comparison of the breakthrough data from the experimental and control columns. Results show that the MO-functionalized adsorbents effectively remove E. coli from contaminated water. Generally, E. coli removal rates were higher in MO-functionalized RHA than in MO-functionalized GAC. These findings suggest the potential use of MO-based biofilters in water disinfection. Due to the low cost and availability of MO in many low-income regions, MO-functionalized adsorbents can be used as an inexpensive water treatment alternative in these areas.
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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) | 0.002 | 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 it