UV‐Sinterable Silver Oxalate‐Based Molecular Inks and Their Application for In‐Mold Electronics
Bibliographic record
Abstract
Abstract A new broadband UV light‐based processing method for a screen printable silver oxalate molecular ink is developed that enables structural electronics to be produced on complex thermoformed 3D objects. The production of these 3D devices is driven by the light‐induced reduction of silver oxalate to form an interfacial silver nanoparticle layer that allows the ink to transition to a viscous liquid intermediate and elongate up to 50% without cracking during thermoforming. Ultimately, this enables the development of 3D electronics devices with significantly less limitations on geometry, shape, size, and depth in comparison to commercially available inks. UV processing is also effective for 2D traces on low temperature PET substrate, where in situ produced silver nanoparticles can subsequently absorb light and further facilitate the rapid conversion of the silver oxalate to metallic silver through a self‐limiting thermal decomposition. The resulting traces have superior electrical properties and are produced in significantly less time in comparison to thermal sintering. Together, UV processing and the silver oxalate molecular ink serve as a platform for both the rapid production of conductive silver traces on low temperature substrates and the development of novel thermoformed 3D human‐machine interface devices, areas of interest for both academia and industry.
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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 | 0.000 |
| 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".