Biogeochemical Defluoridation
Bibliographic record
Abstract
Fluoride in drinking water can lead to a crippling disease called fluorosis. As there is no cure for fluorosis, prevention is the only means of controlling the disease and research into fluoride remediation is critical. This work begins by providing a new approach to assessing fluoride remediation strategies using a combination of groundwater chemistry, saturation indices, and multivariate statistics based on the results of a large groundwater survey performed in a fluoride-contaminated region of India. From the Indian groundwater study, it was noted that one technique recommended for defluoridation involved using hydrous ferric oxide (HFO) as a solid phase sorbent for fluoride. This prompted investigation of bacteriogenic iron oxides (BIOS), a biogenic form of HFO, as a means of approaching bioremediation of fluoride. Batch sorption experiments at ionic strengths ranging from 0.001 to 0.1 M KNO3 and time course kinetic studies with BIOS and synthetic HFO were conducted to ascertain total sorption capacities (ST), sorption constants (Ks), and orders of reaction (n), as well as forward (kf) and reverse (kr) rate constants. Microcosm titration experiments were also conducted with BIOS and HFO in natural spring water from a groundwater discharge zone to evaluate fluoride sorption under field conditions. This thesis contributes significant, new information regarding the interaction between fluoride and BIOS, advancing knowledge of fluoride remediation and covering new ground in the uncharted field of fluoride bioremediation.
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 | 0.001 | 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 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".