Development of in-situ MOF-modified ceramic filters for enhancing fluoride removal in water supply for remote communities
Bibliographic record
Abstract
Ensuring the safe removal of fluoride from drinking water poses a significant challenge in numerous remote communities affected by fluoride contamination. Therefore, this study focuses on the in-situ growth of MOF-AlFu on ceramic filters (CFs), thereby synthesizing in-situ MOF-modified ceramic filters (IMCFs) for effective fluoride removal from drinking water in such communities. Initially, we conducted response surface optimization of the IMCF preparation process, followed by comprehensive characterization. Based on SEM, XRD, and FTIR analyses, it is confirmed that AlFu successfully grows on the surfaces and within the pores of CFs, forming layered structures that enhance the effective adsorption of fluoride on IMCFs. Besides, zeta potential and FTIR results indicated that electrostatic adsorption, ion exchange and hydrogen bonding are the primary mechanisms of fluoride adsorption on IMCFs, which is also confirmed by the adsorption kinetics. The results of adsorption isotherms show that the maximum adsorption of IMCFs is 9.51 mg/g. Within the pH range of 4-10, IMCFs reduced the fluoride concentration from 10 mg/L to below 1.2 mg/L. Furthermore, the IMCF retains over 75 % of its adsorption capacity after undergoing five use cycles, exhibiting remarkable durability during the multiple cycling filtration process. This study suggests that the developed IMCF can serve as a safe and effective defluoridation technology for purifying drinking water in remote communities.
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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".