Shellac-paper composite as a green substrate for printed electronics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Printed electronic (PE) devices that sense and communicate data will become ubiquitous as the Internet of things continues to grow. Devices that are low cost and disposable will revolutionize areas such as smart packaging, but a major challenge in this field is the reliance on plastic substrates such as polyethylene terephthalate. Plastics discarded in landfills degrade to form micro- and nanoplastics that are hazardous to humans, animals, and aquatic systems. Replacing plastics with paper substrates is a greener approach due to the biodegradability, recyclability, low cost, and compatibility with roll-to-roll printing. However, the porous microstructure of paper promotes the wicking of functional inks, which adversely affects printability and electrical performance. Furthermore, truly sustainable PE must support the separation of electronic materials, particularly metallic inks, from the paper substrate at the end of life. This important step is necessary to avoid contamination of recycled paper and/or waste streams and enable the recovery of electronic materials. Here, we describe the use of shellac—a green and sustainable material—as a multifunctional component of green, paper-based PE. Shellac is a cost-effective biopolymer widely used as a protective coating due to its beneficial properties (hardness, UV resistance, and high moisture- and gas-barrier properties); nonetheless, shellac has not been significantly explored in PE. We show that shellac has great potential in green PE by using it to coat paper substrates to create planarized, printable surfaces. At the end of life, shellac acts as a sacrificial layer. Immersing the printed device in methanol dissolves the shellac layer, enabling the separation of PE materials from the paper substrate.
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.
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 | 0.001 | 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) | 0.002 | 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 it