Temperature–Light Dual‐Responsive Au@PNIPAm Core‐Shell Microgel‐Based Optical Devices
Bibliographic record
Abstract
Abstract Au nanoparticle (AuNP) core particles coated with a poly(N‐isopropylacrylamide) (pNIPAm) shell (Au@pNIPAm) are synthesized by seed mediated free radical polymerization. Subsequently, a temperature–light‐responsive photonic device is fabricated by sandwiching the Au@pNIPAm particles between two thin layers of Au. The optical device exhibits visual color and characteristic multipeak reflectance spectra, where peak position is primarily determined by the distance between two Au layers. Dual responsivities of the photonic device are achieved by combining the photothermal effect of AuNPs core (localized surface plasmon resonance (LSPR) effect) and the temperature responsivity of the pNIPAm shell. That is, the pNIPAm shell collapses as the temperature is increased above pNIPAm's lower critical solution temperature, either by direct heat input or heat generated by AuNPs' LSPR effect. To investigate the effect of AuNPs distribution in the microgels on the devices' photothermal responsivity, the Au@pNIPAm microgel‐based etalon devices are compared with that fabricated by AuNP‐doped pNIPAm‐based microgels; in terms of response kinetics and optical spectrum homogeneity. The uniform Au@pNIPAm microgel‐based devices show a fast response and exhibit a comparatively homogeneous spectrum over the whole slide. These materials can potentially find use in drug delivery systems, active optics, and soft robotics.
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 | 0.001 | 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".