Optimized inkjet-printed silver nanoparticle films: theoretical and experimental investigations
Bibliographic record
Abstract
We study the influence of inkjet printing scheme and sintering parameter on the electrical resistivity of multi-layer silver nanoparticle films. A central composite Design Of Experiments (DOE) is employed to maximize experimental efficiency and improve the statistical significance of parameter estimates. The resulting mathematical correlations allow to interpret the influence of the print and sintering parameters. Detailed inspection of the correlations reveals the existence of local extrema and indicates that a structured approach such as the DOE would be significantly more effective for fabricating films with a minimum of resistivity. Furthermore, we modify the well-known Fuchs-Sondheimer Mayadas-Shatzkes model to correlate the resistivity of a multi-layer nanoparticle film with the sintering temperature and time. The modified model uses literature data but one constant inferred from two experiments. After model adjustment, the resistivities of films fabricated with different parameters can be predicted with good accuracy. This validation tremendously increases applicability and relevance of the model.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".