Nitridation of Zerovalent Fe Nanoparticles: A Parametric Study towards Sustainable Synthesis of Fe Nitride Nanomaterials
Bibliographic record
Abstract
Identifying non-toxic, cost-effective, and durable materials is crucial for sustainable economic development.Among candidate materials, iron nitrides exhibit many phases with striking properties and which span a wide range of potential applications, especially at the nanoscale.However, current synthesis methods for iron nitrides rely on high-temperature processes, which are not conducive to sustainable production and prevent a precise control of their composition and structure.This study reports on an alternative low temperature synthesis approach.It involves the preparation of ligand coated zerovalent iron nanoparticles (FeNPs), which are activated at moderate temperatures under hydrogen and nitridated by exposure to ammonia.The effects of ammonia partial pressure, temperature, and flow rate were studied.The synthesized nanomaterials were characterized using transmission electron microscopy (TEM), inductively coupled plasma (ICP) analysis, X-ray diffraction (XRD), and magnetic measurements.We demonstrate that the Fe2N phase can be synthesized in a pure form, and key parameters for tuning the nitrogen content in the NPs are identified.
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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".