Direct writing of stretchable metal flake conductors: improved stretchability and conductivity by combining differently sintered materials
Bibliographic record
Abstract
Abstract Stretchable and flexible electronics with elastic interconnects is critical for electronics to be compatible with wearable applications. Especially fabricating such interconnects with printing technology promises rapid, low-cost, and mass manufacturing. Here, a novel direct-write manufacturing process is presented for a stretchable conductor using an extrusion printer and a micron size silver flake ink as the conductive material without expensive nanomaterials. Conductivity increases as the sintering temperature is increased but at the cost of limited stretchability. To overcome this trade-off, we place one mildly sintered line on top of a fully sintered line to create a hybrid serpentine feature. These two lines of a hybrid feature have two different stretchability-conductivity profiles, synergistically improving both the stretchability and conductivity. With this method, a maximum stretchability of 120% strain is obtained with a minimal change in resistance by a factor of 2.8. This stretchable conductor can endure more than 250 cycles of stretching and releasing under 40% strain. The aggregate electrical conductivity is in the range of 4.74*10 4 S cm −1 , which is far superior to any carbon filler based printable composites.
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".