Towards robust flexible electronics: Fabrication approaches and ongoing research challenges
Bibliographic record
Abstract
Flexible electronics (FEs) revolutionize wearable technology, medical and bio-integrated devices, and robotics systems, allowing soft, lightweight systems to conform to complex surfaces and dynamic environments. They are crucial for soft robots, wearable robots, and bio-integrated systems. However, designing them to be efficient and practically useful to a targeted application has proved challenging. Multiple definitions of flexible electronics components exist in the literature, each representing varying levels of guidelines for the design and construction of stretchable circuits for robots and bio-integrated systems. This paper presents a comprehensive definition of FEs components, intending to provide scientists and engineers with a foundational understanding of these components for their practical design and construction. In robotic applications, one key advantage of FE is its ability to enhance robot traits, making them softer, smarter, and more sensitive, with qualities comparable to those of humans. Despite its advantages, this technology still faces several challenges, such as ensuring sensor precision, scaling up from prototypes to mass production, and integrating FE into soft robots without compromising their flexibility. For such soft robots, it is crucial to have lightweight, durable power sources and the capability to process large amounts of sensor data in real-time to facilitate safe human interaction. The key challenges in FEs include maintaining both electrical conductivity and stretchability, as well as developing materials that are biocompatible and biodegradable for use in medical and wearable robotics. This paper reviews recent state-of-the-art advancements in the fabrication of micro- and nano-scale FE components and highlights key research issues, proposing directions for future research to bridge the knowledge gaps.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".