Optically triggered and spatially controllable shape-memory polymer–gold nanoparticle composite materials
Bibliographic record
Abstract
A new optically triggered shape memory composite material was prepared and investigated. Poly(ε-caprolactone) (PCL)-surface functionalized AuNPs were loaded in a thermosensitive shape-memory polymer (SMP) matrix of biodegradable, branched oligo(ε-caprolactone) (bOCL) cross-linked with hexamethylene diisocyanate (HMDI), referred to as XbOCL. By making use of a localized photothermal effect arising from the SPR absorption of AuNPs, we are able to demonstrate an optically triggered and spatially selective shape recovery process, with a stretched AuNP-loaded XbOCL film undergoing stepwise contraction and lifting of a load. Since the shape recovery process can be halted at any time by turning off the light exposure, multiple intermediate shapes can readily be obtained. These are appealing features that cannot be obtained from thermally activated SMPs based on a bulk thermal effect. Moreover, the magnitude of the photoinduced temperature increase of the material can be controlled by adjusting the laser power, it is also possible to use the same AuNP-loaded composite material for applications with different environmental temperatures below Ttransition, since the thermal transition at T > Ttransition can be optically induced by a laser from different environmental temperatures.
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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".