An Easy Route to Synthesize Novel Fe<sub>3</sub>O<sub>4</sub>@Pt Core-shell Nanostructures with High Electrocatalytic Activity
Bibliographic record
Abstract
In this work, the effect of changing the stirring method and temperature on the physicochemical properties of metallic nanoparticles and core-shell nanostructures is shown. Magnetic (MS), mechanical (UT) and ultrasonic (USS) stirring are the methods of synthesis. The effect that, temperatures between 0 and 50 °C, has on the structure and particle size of Fe3O4 nanoparticles is evaluated. The results indicate that Fe3O4 prepared by the three methods presents a spinel-type crystalline structure. An increase in the synthesis temperature leads to highly crystalline powders. Afterwards, Pt is deposited by the UT method on Fe3O4 to form Fe3O4@Pt core-shell nanostructures. It is important to mention that the time used for the synthesis of the nanoparticles and the core-shell nanostructures is only one minute. The presence of Fe3O4 and Pt is confirmed by XRD and XPS. The metallic Pt phase is confirmed because the binding energy of Pt 4f 7/2 is associated to platinum in the zero-valent state. We evaluated the electrochemical activity of the Fe3O4@Pt core-shell nanostructures for the oxygen reduction reaction (ORR). The novel materials show a high electrocatalytic activity and the Koutecky-Levich analysis indicates that the reaction follows a 4 electron transfer mechanism on the Fe3O4@Pt nanostructures prepared by the three stirring processes. Moreover, the mass specific activity of the core-shell materials is as high as that obtained from the Pt-alone catalysts, suggesting that the amount of Pt in these electrodes can be reduced without decreasing the performance.
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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 | 0.001 |
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".