Designing for Degradation: Transient Devices Enabled by (Nano)Cellulose
Bibliographic record
Abstract
Transient technology involves materials and devices that undergo controlled degradation after a reliable operation period. This groundbreaking strategy offers significant advantages over conventional devices based on non-renewable materials by limiting environmental exposure to potentially hazardous components after disposal, and by increasing material circularity. As the most abundant naturally occurring polymer on Earth, cellulose is an attractive material for this purpose. Besides, (nano)celluloses are inherently biodegradable and have competitive mechanical, optical, thermal, and ionic conductivity properties that can be exploited to develop sustainable devices and avoid the end-of-life issues associated with conventional systems. Despite its potential, few efforts have been made to review current advances in cellulose-based transient technology. Therefore, this review catalogs the state-of-the-art developments in transient devices enabled by cellulosic materials. To provide a wide perspective, the various degradation mechanisms involved in cellulosic transient devices are introduced. The advanced capabilities of transient cellulosic systems in sensing, photonics, energy storage, electronics, and biomedicine are also highlighted. Current bottlenecks toward successful implementation are discussed, with material circularity and environmental impact metrics at the center. It is believed that this review will serve as a valuable resource for the proliferation of cellulose-based transient technology and its implementation into fully integrated, circular, and environmentally sustainable devices.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".