Studies on Surface Facets and Chemical Composition of Vapor Grown One-Dimensional Magnetite Nanostructures
Bibliographic record
Abstract
Investigations on shape and chemical composition of one-dimensional magnetite nanostructures grown by a catalyst-assisted vapor phase procedure are reported. Intrinsic crystal chemistry (preferred growth of most stable surfaces) could be modulated by seeding the magnetite growth through Au nanoclusters, which led to elongated nanostructures (VLS mode); however, the structures have similar facets as observed in uncatalyzed growth. Geometric and energetic contributions to the evolution of the predominately observed {111} surface facets are discussed on the basis of high-angle annular dark field (HAADF) images and electron energy loss spectroscopy (EELS). The Fe:O stoichiometry in magnetite nanowire was determined by EELS, which manifested the reproducibility of nanowire growth by molecule-based CVD and the slightly nonstoichiometric nature of magnetite (Fe 3 O 4−0.15 ). In combination with HAADF-TEM techniques, Au nanoclusters were identified on the surface of single-crystalline nanowires, which ably result from the surface diffusion of the catalyst (Au) material. In addition, core−shell SnO 2 /Fe 3 O 4 1 D nanostructures were fabricated by sequential deposition of Sn and Fe precursors. Cross-sections of the coaxial nanostructures revealed polycrystalline magnetite shells on single-crystalline SnO 2 wires constituted by well-defined single-crystalline facetted grains of slightly nonstoichiometric magnetite.
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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) | 0.000 | 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 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".