Deflouridation from Aqueous Solutions Using Alum
Bibliographic record
Abstract
This research work has been designed to remove the fluoride from aqueous solutions using alum by bench scale experiments.The defluoridating agent, which is easily available even in rural areas, has been selected.The known concentrations of fluoride solution were prepared.The removal of fluorides by the defluoridating agent was studied up to 4 hours for all the fluoride concentrations.The variations in the percentage removal and attainment of equilibrium were recorded.The solutions of 2, 4, 6, 8 and 10 mg/L were prepared.Each Fluoride concentration was tested with 100, 200 and 300 mg/L of alum.The removal of fluoride increased at the rate of 10% per hour up to 55% by 4 h for 100 and 200 mg/L while it rose from 40 to 60% by 4 h equilibration time in 300 mg/L alum solution it reaches 60%.The difference of fluoride removal between 100 and 300 mg/L alum concentrations was only 5% i.e. 0.9 and 0.8 mg/L of fluoride remained after 4 h equilibration time.All the concentrations of defluoridating agent have successfully reduced the fluoride content in waters to permissible limits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".