Zirconium-based metal organic framework as novel adsorbent for Indigo Carmine
Bibliographic record
Abstract
Drinking water is a vital component for sustaining life on our planet. Unfortunately, the presence of numerous recalcitrant organic and inorganic contaminants in the aquatic environment cause adverse health effects in humans, rendering water consumption dangerous. Therefore, it has become urgent to develop and establish treatment methods to decontaminate waters from persistent chemicals. Adsorption is such a promising method, mainly due to its low-cost, high performance, easiness of operation, and effectiveness in a wide pH range. The current research work focuses on the effectiveness of a novel Zirconium-based metal organic framework (MOF) solvothermally synthesized in our laboratory to remove Indigo Carmine from water. Indigo Carmine is a water-soluble organic salt used as colorant in pharmaceuticals, foods, and cosmetics; however, its presence in water has been associated with hypertension, cardiovascular and respiratory effects, carcinogenesis, and neurotoxicity. MOFs are novel compounds consisting of metals ions coordinated to organic ligands. They possess high surface areas and porosity, which classify them as ideal for the adsorption of various water contaminants. This work aims to develop comprehensive insights that will lead to more advanced efficient adsorption processes.
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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) | 0.001 | 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".