MCM41/Fe3O4/EDTA Materials from Removal Different Cation from Waste Water
Bibliographic record
Abstract
MNPs functionalization with different materials (silica network, carboxylic acid, amino acids, organic acid, polymers), lead to advance materials with core-shell structure, these structuresbeing potential candidates for waste water decontamination.The advantages that recommend these structures are especially related to the improved dispersion and stability of core shell nanostructures during application and, the shell can also act as an active layer which can bind, selectivelly, desired species.Mesoporous silica such as MCM 41, MCM 48, are used widely for removal of environmental pollutants, (especially heavy metals), due to very low toxicity, and high capacity for adsorbing [1].In this work the synthesis and characterization of MCM41/Fe 3 O 4 /EDTA are presented, their final purpose being to be used in removing different cation from waste water and water softeners.The materials synthesis was done in two stages.First by the Fe 3 O 4 /EDTA was obtained by co-precipitation method in alkaline media.Second by MCM41/Fe 3 O 4 /EDTA was obtained by sol-gel method.The obtained materials were characterized by FT-IR spectroscopy, scanning electron microscopy, XRD-diffraction, BET etc.The absorption capacity was evaluated by UV-Vis spectroscopy.
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) | 0.002 | 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 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".