Bibliographic record
Abstract
Abstract Potential for integrated flexible electronics has been shown for 2D materials with atomically thin layers and dangling‐bond‐free surfaces. Strain engineering is a fascinating technique for tuning or controlling the electronic and optical properties of 2D materials. In the review article, an effort to condense the most recent and promising approaches that can be used to create flexible nanoelectronic and optoelectronic devices in the future and expand their range of useful applications is made. In order to investigate their electrical behavior, the majority of the devices are created using chemical vapor deposition (CVD) or mechanical exfoliation of ultrathin 2D TMD materials. This makes it possible to develop flexible piezo‐phototronic photodetectors, self‐powered sensors, and wearable and implantable devices with higher strain tolerance. On the basis of 2D TMDs materials, a comparison of the performance and characteristics of 2D flexible electronic devices is also carried out. In order to advance the development of wearable and implantable electronics with greater strain tolerance for health monitoring and usher in a new era of flexible technologies, this overview of recent research on 2D flexible electronics based on nanomaterials is intended to aid in the advancement of the field. Finally, it summarizes the current challenges and opportunities.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".