EDTA-Na<sub>3</sub>functionalized Fe<sub>3</sub>O<sub>4</sub>nanoparticles: grafting density control for MRSA eradication
Bibliographic record
Abstract
We report a synthesis strategy to simplify often cumbersome post-synthesis ligand exchange protocols and use that approach to synthesize EDTA-Na3 (N-(trimethoxysilylpropyl)ethylenediaminetriacetate, trisodium salt) functionalized hydrophilic and biocompatible Fe3O4 nanoparticles. The grafting density of EDTA-Na3 has been controlled from 0.07-0.37 μmol m-2 by varying the time at which EDTA-Na3 was added to the reaction. The success of EDTA-Na3 surface functionalization has been verified using Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA) and Mössbauer spectroscopy techniques. Mössbauer spectroscopy results showed the evidence of Fe-EDTA monomer and dimer formation signifying covalent bonding between Fe ions and EDTA-Na3. The earliest addition of EDTA-Na3 resulted in the most stable dispersion of nanoparticles in water and phosphate buffered saline (PBS) which remained stable for more than a month. In addition, our results suggest that these nanoparticles can have useful applications in magnetic hyperthermia and eradication of methicillin-resistant Staphylococcus aureus (MRSA) bacteria in presence of an ac magnetic field.
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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) | 0.000 | 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".