Nanoscale Analysis of Surface Bending Strain in Film Substrates for Preventing Fracture in Flexible Electronic Devices
Bibliographic record
Abstract
Abstract With the rapid development of flexible electronics and soft robotics, there is an emerging topic of preventing fracture in materials and devices integrated on largely bending film substrates of >100 µm thickness. The high demand for strategically reducing strain in bending materials requires a facile method that enables one to accurately and precisely analyze the surface bending strain in a wide variety of materials. This study proposes the surface‐labeled grating method that is the fundamental and efficient technique for measuring surface bending strains merely by labeling a thin, soft grating onto various film substrates composed of flexible polymeric and rigid inorganic materials. The surface strain with a single‐nanoscale (<1.0 nm) can be quantified in real time with no need of material information such as Poisson's ratio, Young's modulus, and film thickness. The fracture limit of a hard coating overlying flexible substrates is successfully determined by the accurate and precise quantification of surface bending strains. Furthermore, a multilayer film substrate with surface bending strain reduced by 50% prevents fractures of hard coatings and organic thin film transistors (OTFTs) since the strains remain below the fracture limit under large bending.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".