Formation Mechanisms of Co-existence of α-Fe and Iron Oxides Nanoparticles Decorated on Carbon Nanofibers by a Simple Liquid Phase Adsorption-Thermal Oxidation
Bibliographic record
Abstract
We propose formation mechanisms of co-existence of α-Fe and iron oxides nanoparticles decorated on CNFs. The α-Fe nanoparticles are produced via oxidation-reduction mechanisms, which occur in liquid phase adsorption (LPA) assisted by ultrasonic energy, while α-Fe2O3 nanoparticles are thermally formed through mechanisms of Lewis acidbase. In addition, Fe3O4 is thermally formed by reducing Fe2O3 by CNFs. Liquid phase adsorption assisted by ultrasonic energy under ambient temperature using Fe(NO3)3•9H2O as a precursor of iron oxides and α-Fe has been applied. Then, as prepared, Fe(III)@CNFs were thermally calcined at 573 K under air atmosphere in various holding times ranging from 0.5 to 2 h. XRD data confirmed that α- Fe2O3 and Fe3O4 had been successfully grown onto CNFs. Moreover, the presence of the iron oxides and iron nanoparticles was studied by the SEMEDX technique. The iron oxide nanoparticles appeared after a heating period of 0.5h. However, at a holding time of 0.5 h, we found an exciting and unexpected phenomenon where oxygen content is zero percent while Fe is 0.23 wt %. It implies that α-Fe nanoparticles were formed earlier than α-Fe2O3 and Fe3O4 as the proposed mechanisms. Formation mechanisms of iron and its oxides such as α-Fe2O3 and Fe3O4 decorated on CNFs through liquid-phase adsorption followed by thermally treatment technique in this work is expected to give significant contribution in the field of nanocomposite materials, especially for anode materials based on iron oxides.
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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 | 0.000 | 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 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".