Programmable Assembly of π‐Conjugated Polymers
Bibliographic record
Abstract
π-Conjugated polymers have numerous applications due to their advantageous optoelectronic and mechanical properties. These properties depend intrinsically on polymer ordering, including crystallinity, orientation, morphology, domain size, and π-π interactions. Programming, or deliberately controlling the composition and ordering of π-conjugated polymers by well-defined inputs, is a key facet in the development of organic electronics. Here, π-conjugated programming is described at each stage of material development, stressing the links between each programming mode. Covalent programming is performed during polymer synthesis such that complex architectures can be constructed, which direct polymer assembly by governing polymer orientation, π-π interactions, and morphological length-scales. Solution programming is performed in a solvated state as polymers dissolve, aggregate, crystallize, or react in solution. Solid-state programming occurs in the solid state and is governed by polymer crystallization, domain segregation, or gelation. Recent progress in programming across these stages is examined, highlighting order-dependent features and assembly techniques that are unique to π-conjugated polymers. This should serve as a guide for delineating the many ways of directing π-conjugated polymer assembly to control ordering, structure, and function, enabling the further development of organic electronics.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".