Phase-Induced Shape Evolution of FeNi Nanoalloys and Their Air Stability by in-Situ Surface Passivation
Bibliographic record
Abstract
Shape and size of nanoparticles are fundamental structural properties that govern the development of novel surface-related applications. Traditionally external agents such as surfactants, reducing agents, or stabilizers have been used for enforcing preferential growth orientation, size, and shape to develop tailor-made nanoparticles. However, these external agents cover the pristine surface of the particle and invariably reduce the surface activity. Here, we introduce a surfactant-free, single-step electrochemical method to control the shape of air-stable FeNi alloy nanoparticles. Using glancing-incidence X-ray diffraction, we further demonstrate that the shape evolution of nanoparticles from concave cube to truncated sphere occurs concurrently with the phase transformation from bcc to fcc. This shape evolution can be achieved by fine-tuning a single parameter, the ratio of reactant concentrations (i.e., [Ni 2+ ]/[Fe 2+ ]). Addition of Ni 2+ to the Fe 2+ electrolyte changes the nucleation mechanism from progressive growth for pure Fe 2+ electrolyte to instantaneous growth for mixed Fe 2+ /Ni 2+ electrolyte, which leads to a remarkably narrow size distribution and very uniform dispersion on the Si substrate. Depth-profiling X-ray photoelectron spectroscopy and energy-dispersive X-ray analysis by both transmission electron microscopy and scanning electron microscopy for nanoparticles at different growth stages reveal alloy formation and preferential deposition of Fe during initial growth that results in a quasi-core–shell structure. We also observe the in-situ formation of a very thin Ni-doped FeOOH outer layer and NiFe 2 O 4 intermediate layer on the skin of the nanoparticles, which passivates the surface and dramatically enhances the air stability. The present work provides a unique example of shape-controlled bimetallic nanostructures and offers insights into growth modification of a host metal structure by a guest metal.
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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 | 0.000 |
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".