Influence of the Crystallinity of Silver Nanoparticles on Their Magnetic Properties
Bibliographic record
Abstract
Abstract The magnetic properties of noble‐metal nanoparticles are a puzzling phenomenon, tentatively often explained as a size effect or a ligand effect. Many experimental studies performed to date have attempted to vary these readily available parameters without reaching a definitive conclusion. In an attempt at better understanding the role of core crystallinity on these magnetic properties, we have compared the behavior of silver nanoparticles, which were either single‐crystalline or multi‐twinned, of almost identical sizes and with the same ligand coating. Our results indicate that single‐crystalline nanoparticles tend to behave as classical paramagnetic materials, whereas multi‐twinned ones exhibit a combination of para‐ and ferro‐magnetic behaviors. Our hypothesis is that lattice defects within the core bear magnetic moments which couple through conduction electrons, with dipolar interactions also playing a local and macroscopic role.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 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 teacher head, 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".