Importance of the Inter-Electrode Distance for the Electrochemical Synthesis of Magnetite Nanoparticles: Synthesis, Characterization, Computational Modelling, and Cytotoxicity
Bibliographic record
Abstract
Magnetite (Fe3O4) nanoparticles, are promising inorganic nanomaterials for future biomedical applications due to their low toxicity and unique magnetic properties. However, the synthesis of these particles can often be expensive, energy intensive, and non-scalable, requiring the addition of surfactants to stabilize the material to control the particle size and avoid agglomeration. We wish to report a simple, green, surfactant-free electrochemical synthesis of these materials using a closed aqueous system at ambient temperature. Particle diameter, between 19 and 33 nm, was controlled by simply modifying the distance between the electrodes. These magnetite nanoparticles were then fully characterized using both spectroscopy and microscopy. Vibrational magnetometry indicates that as the size of the particle decreases, the magnetic hysteretic gap decreases, although for samples below 25 nm no inter-sample difference was observed. To support this experimental data, we carried out a Density Functional Theory (DFT) analysis of magnetite containing more than three iron atoms in the cluster, an essential proposition as magnetite contains three distinct iron species. These calculations were used to support the experimental observations, and closely reproduced both the experimental IR spectrum, and the XRD pattern. In vitro cytotoxicity assays showed dose responsive behavior for the nanoparticles, and demonstrated that they are non-toxic at clinically relevant concentrations; below 200 μg/mL we observed no toxicity in a 48-hour standard assay. This work represents the first DFT based simulation of this detailed magnetite cluster, and demonstrates that this sustainable synthetic method is capable of producing nanomaterials with a physical and biological profile that might make them suitable for biomedical applications. [DOI: 10.1380/ejssnt.2017.31]
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".