Revolutionizing wearable technology: advanced fabrication techniques for body-conformable electronics
Bibliographic record
Abstract
With the increasing demand for wearable electronic products, there is a pressing need to develop electronic devices that seamlessly conform to the contours of the human body while delivering excellent performance and reliability. Traditional rigid electronic fabrication technologies fall short of meeting these requirements, necessitating the exploration of advanced flexible fabrication technologies that offer new possibilities for designing and fabricating flexible and stretchable electronic products, particularly in wearable devices. Over time, the continuous development of innovative fabrication techniques has ushered in significant improvements in the design freedom, lightweight, seamless integration, and multifunctionality of wearable electronics. Here, we provide a comprehensive overview of the advancements facilitated by advanced fabrication technology in wearable electronics. It specifically focuses on key fabrication methods, including printed electronics fabrication, soft transfer, 3D structure fabrication, and deformation fabrication. By highlighting these advancements, it sheds light on the challenges and prospects for further development in wearable electronics fabrication technologies. The introduction of advanced fabrication technologies has revolutionized the landscape of wearable/conformable electronics, expanding their application domains, streamlining system complexity associated with customization, manufacturing, and production, and opening up new avenues for innovation and development of body-conformable electronics.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".