Programmable Wrinkling of Self-Assembled Nanoparticle Films on Shape Memory Polymers
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".