Neighbors Matter: Leveraging Collective Thermoplasmonic Effects for Smart Soft Actuators
Bibliographic record
Abstract
In this work, we are exploring the collective thermoplasmonic properties of small spherical gold nanoparticles embedded into poly( N -isopropylacrylamide) (pNIPAM) films, with a focus on their application as efficient light-driven nanoheaters for smart soft actuation systems. Uniform Au-pNIPAM hybrid films with adjustable thickness in the micrometer range (1–22 μm) and distinct concentrations of gold nanoparticles were fabricated by using a photopolymerizable pNIPAM-based resin containing Au-pNIPAM core–shell microgels as building blocks (15 nm diameter gold cores). Upon 520 nm light excitation, the Au-pNIPAM films exhibit a significant temperature increase of up to 75 °C above room temperature at a light irradiance of 116 mW/mm 2, as determined by thermal imaging. These results compare well with those obtained with an analytical model describing the rise in temperature produced by neighboring particles in a three-dimensional (3D) matrix under continuous illumination, with a relative margin of error of less than 7% for nearly all cases studied. Finally, light-guided swimming robots were fabricated by leveraging the collective photothermal properties of gold nanoparticles in the Au-pNIPAM films. Under light exposure, the trajectory and rotation of swimming robots floating at the air/water interface can be precisely controlled due to the light-induced Marangoni effect, with average speeds of up to 2.5 mm/s for triangular-shaped robots.
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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".