Molecular Considerations for Mesophase Interaction and Alignment of Lyotropic Liquid Crystalline Semiconducting Polymers
Bibliographic record
Abstract
Intermolecular interactions in conjugated polymers influence crystallinity, self-assembly, and packing motif, factors which in turn crucially impact charge transport properties such as carrier mobility in organic electronic devices. Correlated alignment of molecular and crystalline morphologies provides direct pathways for charge carriers to follow; however, the role of intermolecular interactions in achieving this is unexplored. Herein, we synthesize a series of lyotropic liquid crystalline conjugated polymers with variable side-chain structure to lend distinct steric repulsion and van der Waals attractive forces to each mesophase. We use this to investigate the role of intermolecular interactions on mesophase alignment. The strength of intermolecular interaction for each mesophase is compared by measuring melting temperature, π-stacking distance, and the Maier-Saupe interaction parameter. In general we find that side-chain structure can impact interaction strength by varying steric repulsion and backbone attractions and that the Maier-Saupe interaction parameters correlate with higher degrees of alignment after shearing, achieving a dichroic absorbance ratio of up to 2. This observation is used to develop equilibrium processing methods for fabricating macroscopically aligned polymer substrates used in transistors, improving mobility by a factor of 3 compared to spin-coated 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".