(Invited) Energy Transfer in Stimuli-Responsive Multiblock Nanofibers from Organic Electronic Materials
Bibliographic record
Abstract
Techniques for the assembly of hierarchical nanostructures from soft matter have opened the door to many new applications of nanotechnology. Despite these achievements, nanoscale syntheses that rely on self-assembly can be highly dependent on conditions such as solvent and temperature if the integrity of the nanomaterial is to be maintained. Bottlebrush copolymers provide a compelling bottom-up approach to the synthesis of hierarchical nanostructures from soft material, allowing for the preparation of multicompartment structures that remain nanosegregated by virtue of their covalent chemistry. Here we describe methods for the preparation of fiber-like nanomaterials that mimic the multilayer structure of organic electronic devices on individual polymer chains. Narrowly dispersed fibers are prepared from materials commonly used as the hole transport, electron transport, and host materials in organic electronics, with molecular weights on the order of 106 Da. Energy transfer interactions can be regulated by the reversible expansion and collapse of polymer chains, creating dramatic fluorescence changes or activating thermaly activated delayed fluorescence (TADF).
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".