Tuning the magnetism of gold nanoparticles by changing the thiol coating
Bibliographic record
Abstract
Room-temperature ferromagnetic behaviour has been reported in nanoscale materials expected to be diamagnetic, including gold. However, it is yet unclear which factors (size, shape, surface coating) predominantly influence the magnitude of the magnetic response. In this work, we study the magnetic and electronic properties of similarly-sized gold nanoparticles (Au NPs) coated with four different n-alkanethiols, as well as hydroxyl- and carboxyl-functionalized alkanethiols using superconducting quantum interference device (SQUID) magnetometry and ultraviolet photoelectron spectroscopy (UPS). We find room-temperature behaviour (hysteresis in magnetization vs. field strength loops) in all samples, as well as large effective magnetic anisotropy. Importantly, we find the nanoparticles coated with polar chain end-groups (-OH and -COOH) show markedly higher magnetization; this increased magnetization correlates with a higher work function. This work establishes chemical handles to enhance magnetism in nanoscale gold particles.
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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 | 0.000 | 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 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".