Shrinking Devices: Shape‐Memory Polymer Fabrication of Micro‐and Nanostructured Electrodes
Bibliographic record
Abstract
Abstract Since their discovery in the 1940s, shape memory polymers (SMPs) have been used in a broad spectrum of applications for research and industry.[1] SMPs can adopt a temporary shape and promptly return to their original form when submitted to an external stimulus. They have proven useful in fields such as wearable and stretchable electronics,[2] biomedicine,[3] and aerospace..[4] These materials are attractive and unique due to their ability to “remember” a shape after being submitted to elastic deformation. By combining the properties of SMPs with the advantages of electrochemistry, opportunities have emerged to develop structured sensing devices through simple and inexpensive fabrication approaches. The use of electrochemistry for signal transduction provides several advantages, including the translation into inexpensive sensing devices that are relatively easy to miniaturize, extremely low concentration requirements for detection, rapid sensing, and multiplexed detection. Thus, electrochemistry has been used in biosensing,[5] pollutant detection,[6] and pharmacological[7] applications, among others. To date, there is no review that summarizes the literature addressing the use of SMPs in the fabrication of structured electrodes for electrochemical sensing. This review aims to fill this gap by compiling the research that has been done on this topic over the last decade.
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 | 0.001 |
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".