Laser-Assisted Synthesis of Superparamagnetic Fe@Au Core−Shell Nanoparticles
Bibliographic record
Abstract
A novel method combining wet chemistry for synthesis of an Fe core, 532 nm laser irradiation of Fe nanoparticles and Au powder in liquid medium for deposition of an Au shell, and sequential magnetic extraction/acid washing for purification has been developed to fabricate oxidation-resistant Fe@Au magnetic core-shell nanoparticles. The nanoparticles have been extensively characterized at various stages during and up to several months after completion of the synthesis by a suite of electron microscopy techniques (HRTEM, HAADF STEM, EDX), X-ray diffraction (XRD), UV-vis spectroscopy, inductively coupled plasma atomic emission spectroscopy, and magnetometry. The surface plasmon resonance of the Fe@Au nanoparticles is red shifted and much broadened as compared with that of pure colloidal nano-gold, which is explained to be predominantly a shell-thickness effect. The Au shell consists of partially fused approximately 3-nm-diameter fcc Au nanoparticles (lattice interplanar distance, d = 2.36 A). The 18-nm-diameter magnetic core is bcc Fe single domain (d = 2.03 A). The nanoparticles are superparamagnetic at room temperature (300 K) with a blocking temperature, T(b), of approximately 170 K. After 4 months of shelf storage in normal laboratory conditions, their mass magnetization per Fe content was measured to be 210 emu/g, approximately 96% of the Fe bulk value.
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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".