Grain Structure Engineering in Screen-Printed Silver Flake-Based Inks for High-Temperature Printed Electronics Applications
Bibliographic record
Abstract
We extensively studied serigraphic screen-printed commercial silver flake inks loaded with silicon inclusions in order to achieve pinning at the grain boundaries. Based on grain size measurements using electron backscattered diffraction (EBSD), commercial silver ink with silicon microparticle content of 5 wt.% shows significant grain growth retardation compared to pristine silver ink, which stabilizes electrical conductivity up to 700 °C via a Zener pinning mechanism. The modified silicon-loaded silver ink experiences a two-times increase in grain size when heated up to 700 °C, compared to a seven-times increase for pristine silver ink. In turn, this enables operation temperatures significantly higher than the conventional operational window of microparticle-based silver inks, which are usually limited to 400 °C. Using isothermal exposures of 10 min up to 4 h, this phenomenon is observed at temperatures ranging from 250 °C to 900 °C. The electrical conductivity stability, grain size evolution and oxide contents were studied up to 4 h. The activation energy of silver ink with silicon inclusions is 54% lower than for pristine silver ink due to the pining effect, which retards grain growth via the Zener mechanism. Most importantly, the electrical resistivity remains stable up to 700 °C, which is more than twice the operation limit for off-the-shelf screen-printable silver flake inks. Hence, we demonstrate that adding controlled amounts of silicon particles to silver inks for grain structure engineering can open new vistas of possibilities for screen-printed metallic inks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".