Elucidating the Role of Hydrogen Bonds for Improved Mechanical Properties in a High-Performance Semiconducting Polymer
Bibliographic record
Abstract
Incorporation of hydrogen bond moieties into the backbone or side chain of conjugated polymers is an effective strategy to enhance mechanical performance, facilitate morphological organization, and promote self-healing ability. However, the understanding of hydrogen bonds, particularly the effect of bond strength and directionality, on thermomechanical and optoelectronic performance is still in its infancy due to the competing influence of morphology, glass transition phenomena, and the measurement process itself. Here, we compare the influence of statistically incorporated amide and urea moieties on the mechanical properties of DPP-TVT parent polymers. We observed a profound difference in ductility; amide functionalization increases the strain at failure by over 100% relative to the pure DPP-TVT polymer, while urea functionalization results in a loss of strain at failure by 50%. This is attributed to the crystalline behavior of functionalized conjugated polymers that is promoted by intermolecular interactions of urea groups, which we elucidated via an in-depth investigation of the swelling, crystalline packing, thermal behavior, and strain-dependent charge transport. Furthermore, we employed a novel free-standing tensile test to validate our mechanical measurements supported on a water surface. Our results demonstrated that hydrogen bond moieties must be carefully chosen to achieve a delicate balance of morphological control and mechanical performance, as simply increasing the hydrogen bond strength can result in detrimental mechanical and electrical performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".