Thin-Film Fracture Behavior for Diketopyrrolopyrrole Semiconducting Polymeric Films
Bibliographic record
Abstract
Fracture energy, which quantifies a material's resistance to the propagation of a pre-existing crack, is a key parameter for ensuring the mechanical reliability of stretchable organic electronic devices. However, most existing methods, such as a four-point bending fracture energy, utilized for measuring the fracture energy of semiconducting polymeric thin films are complicated by substrate effects, making it challenging to isolate the intrinsic behavior of the film from interfacial influences. In this study, we employed a pseudo free-standing pure shear method to systematically investigate the cohesive fracture energy of poly-(diketopyrrolopyrrole-terthiophene) P-(DPP-T)-based thin films to examine the effects of nanoconfinement, side chain length, degree of crystallinity, and strain rates. This method effectively eliminates substrate interference, enabling a direct assessment of the cohesive fracture energy of P-(DPP-T) thin films. We found that thinner films and those with lower molecular weights exhibited significantly reduced fracture energies due to diminished chain entanglements. Additionally, films with shorter side chains displayed notably higher fracture energies, which were attributed to an increase in the degree of crystallinity. Finally, slower strain rates led to higher fracture energies, consistent with an enhanced stress relaxation. These insights offer practical guidelines for designing mechanically robust semiconducting polymers, contributing to the advancement of reliable, durable, flexible, and wearable electronic 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) | 0.000 | 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".