Iron detection and remediation with a functionalized porous polymer applied to environmental water samples
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Iron is one of the most abundant elements in the environment and in the human body. As an essential nutrient, iron homeostasis is tightly regulated, and iron dysregulation is implicated in numerous pathologies, including neuro-degenerative diseases, atherosclerosis, and diabetes. Endogenous iron pool concentrations are directly linked to iron ion uptake from environmental sources such as drinking water, providing motivation for developing new technologies for assessing iron(ii) and iron(iii) levels in water. However, conventional methods for measuring aqueous iron pools remain laborious and costly and often require sophisticated equipment and/or additional processing steps to remove the iron ions from the original environmental source. We now report a simplified and accurate chemical platform for capturing and quantifying the iron present in aqueous samples through use of a post-synthetically modified porous aromatic framework (PAF). The ether/thioether-functionalized network polymer, PAF-1-ET, exhibits high selectivity for the uptake of iron(ii) and iron(iii) over other physiologically and environmentally relevant metal ions. Mössbauer spectroscopy, XANES, and EXAFS measurements provide evidence to support iron(iii) coordination to oxygen-based ligands within the material. The polymer is further successfully employed to adsorb and remove iron ions from groundwater, including field sources in West Bengal, India. Combined with an 8-hydroxyquinoline colorimetric indicator, PAF-1-ET enables the simple and direct determination of the iron(ii) and iron(iii) ion concentrations in these samples, providing a starting point for the design and use of molecularly-functionalized porous materials for potential dual detection and remediation applications.
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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) | 0.001 | 0.001 |
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