Hybrid Electroactive Shape Memory Polymer Composites with Room Temperature Deformability
Bibliographic record
Abstract
Abstract Polymeric blend shape memory polymers (SMPs) can be constructed from two immiscible polymeric matrices. The shape recovery behavior of these composite systems can be easily controlled by varying the ratio of the polymer blends. It has been recently discovered that the functionality of SMPs can be further enhanced with electroactive ability through the use of conductive fillers. However, the fillers may negatively interact with the SMPs and cause a reduction in the elongation at failure thereby diminishing the shape recovery performance. It is proposed that a plasticizer can be utilized to alter the microstructure of the SMPs with conductive fillers. In this study, a new hybrid SMP is developed by combining single‐walled carbon nanotubes (SWCNT) into a poly(lactic acid) (PLA) and thermoplastic polyurethane (TPU) SMP system containing poly(ethylene glycol) (PEG) plasticizer. The incorporation of PEG is able to lower the activation temperature, while enhancing dispersion of SWCNT. The presence of SWCNT can stabilize the SMP system and significantly enhance the shape‐fixing capability after deformation at room temperature conditions. By carefully controlling the formulation, an electroactive SMP can be created by optimizing the amount of SWCNT and PEG plasticizer.
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".