Strain engineering in van der Waals materials towards flexible electronics and optoelectronics
Bibliographic record
Abstract
Flexible electronics and optoelectronics are rapidly advancing toward multifunctional integration, high sensitivity, and low power consumption, enabling next-generation technologies in wearable sensing, energy harvesting, and intelligent systems. Van der Waals (vdW) materials, with their exceptional mechanical flexibility and tunable electronic and exceptional optoelectronic properties, form a promising foundation for flexible platforms, particularly when enhanced through strain engineering. While existing reviews have thoroughly explored property modulation in vdW materials, the complex relationship between these modulated properties and the resulting device performance has yet to be fully examined. This review presents a comprehensive analysis that unifies these interrelated elements, including strain application strategies, modulation of physical properties, and device-level implementation, into a cohesive framework for the design and optimization of high-performance flexible vdW electronic and optoelectronic systems. Finally, we summarize the key challenges and outline practical strategies to support the development of next-generation flexible vdW applications that seamlessly integrate multimodal sensing, memorizing, and computing, thereby enabling intelligent, adaptive, and scalable system architectures. • Novel strain application strategies in van der Waals materials were summarized • A unified cohesive framework, integrating strain engineering techniques, property modulation, and device performance optimization was established • The critical role and unique advantages of strain engineering in advancing next-generation flexible vdW electronics and optoelectronics were elucidated • Key challenges hindering future progress were identified, and feasible solutions were proposed
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".