Pattern-dependent resistivity variations in inkjet-printed conductors due to non-uniform ink drying
Bibliographic record
Abstract
Abstract When fabricating inkjet-printed electronic devices and circuits, inkjet-printed conductive materials require drying and sintering to improve electrical conductivity. Electrical conductivity should be the same irrespective of pattern design, size, location, or density of adjacent patterns. However, we demonstrate that spatial variations in the drying process for inkjet-printed patterns with proximity to others cause resistivity variations. These resistivity variations are studied here experimentally for different circuit patterns and in arrays of inkjet-printed square electrodes. This variation depends not only on the location of each electrode in an array but also on the number of electrodes. This means that for the same drying temperature and duration, the array with a larger number of electrodes exhibits a larger resistivity variation. The sooner an electrode dries, the lower resistivity it achieves. The resistivity variation between an individual electrode and the center electrode in a 7 × 7 electrode array can be a factor of seven. This variation decreases for lower numbers of electrodes to a factor of three for a 3 × 3 array. Furthermore, x-ray photoelectron spectroscopy analyses provide evidence for the residual presence of carbon-based materials within electrodes after the drying process. These results confirm that the location of electrodes within an array significantly influences the amount of residual carbon-based materials, thereby contributing to resistivity variations. Although intense pulsed light sintering can decrease this variation, its optimal parameters depend on the printed designs, and our simulation results show a non-uniform temperature profile over the electrode arrays. Temperature increases more at the center of patterns than the corners, which can be useful in this case to improve resistivity uniformity. In this study, for the first time, we show how different printed shapes and designs can result in non-uniform resistivity after drying and sintering.
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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 | 0.000 | 0.001 |
| 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".