Light‐Driven Self‐Oscillation of Thermoplasmonic Nanocolloids
Bibliographic record
Abstract
Self-oscillation-the periodic change of a system under a non-periodic stimulus-is vital for creating low-maintenance autonomous devices in soft robotics technologies. Soft composites of macroscopic dimensions are often doped with plasmonic nanoparticles to enhance energy dissipation and generate periodic response. However, while it is still unknown whether a dispersion of photonic nanocrystals may respond to light as a soft actuator, a dynamic analysis of nanocolloids self-oscillating in a liquid is also lacking. This study presents a new self-oscillator model for illuminated colloidal systems. It predicts that the surface temperature of thermoplasmonic nanoparticles and the number density of their clusters jointly oscillate at frequencies ranging from infrasonic to acoustic values. New experiments with spontaneously clustering gold nanorods, where the photothermal effect alters the interplay of light (stimulus) with the disperse system on a macroscopic scale, strongly support the theory. These findings enlarge the current view on self-oscillation phenomena and anticipate the colloidal state of matter to be a suitable host for accommodating light-propelled machineries. In broad terms, a complex system behavior is observed, which goes from periodic solutions (Hopf-Poincaré-Andronov bifurcation) to a new dynamic attractor driven by nanoparticle interactions, linking thermoplasmonics to nonlinearity and chaos.
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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".