A New Class of Electronic Devices Based on Flexible Porous Substrates
Bibliographic record
Abstract
With the advent of the Internet of Things era, the connection between electronic devices and humans is getting closer and closer. New-concept electronic devices including e-skins, nanogenerators, brain-machine interfaces, and implantable medical devices, can work on or inside human bodies, calling for wearing comfort, super flexibility, biodegradability, and stability under complex deformations. However, conventional electronics based on metal and plastic substrates cannot effectively meet these new application requirements. Therefore, a series of advanced electronic devices based on flexible porous substrates (e.g., paper, fabric, electrospun nanofibers, wood, and elastic polymer sponge) is being developed to address these challenges by virtue of their superior biocompatibility, breathability, deformability, and robustness. The porous structure of these substrates can not only improve device performance but also enable new functions, but due to their wide variety, choosing the right porous substrate is crucial for preparing high-performance electronics for specific applications. Herein, the properties of different flexible porous substrates are summarized and their basic principles of design, manufacture, and use are highlighted. Subsequently, various functionalization methods of these porous substrates are briefly introduced and compared. Then, the latest advances in flexible porous substrate-based electronics are demonstrated. Finally, the remaining challenges and future directions are discussed.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".