Assessment of Moringa-functionalized carbon based biofilter for disinfection through column experiments
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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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) | 0.001 | 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 | 0.001 | 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".