Programmable Wrinkling of Self-Assembled Nanoparticle Films on Shape Memory Polymers
Why this work is in the frame
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Bibliographic record
Abstract
Hierarchically structured materials, inspired by sophisticated structures found in nature, are finding increasing applications in a variety of fields. Here, we describe the fabrication of wrinkled gold nanoparticle films, which leverage the structural tunability of gold nanoparticles to program the wavelength and amplitude of gold wrinkles. We have carefully examined the structural evolution and tuning of these wrinkled surfaces through varying nanoparticle parameters (diameter, number of layers, density) and substrate parameters (number of axes constrained during wrinkling) through scanning electron microscopy and cross-sectional transmission electron microscopy. It is found that nanoparticle layers of sufficient density are required to obtain periodical wrinkled structures. It was also found that tuning the nanoparticle diameter and number of layers can be used to program the wrinkle wavelength and amplitude by changing the film thickness and mechanical properties. This dual degree of tunability, not previously seen with continuous films, allows us to develop one of the smallest wrinkles developed to date with tunability in the sub-100 nm regime. The effect of the induced structural tunability on the enhancement of the intensity of the 4-mercaptopyridine Raman spectra is also studied through the application of these devices as substrates for surface-enhanced Raman spectroscopy (SERS), where wrinkling proves to be an effective method for enhancing the SERS signal in cases where there is an inherently low density of gold nanoparticles.
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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 it