Small‐Peptide‐Based Organogel Kit: Towards the Development of Multicomponent Self‐Sorting Organogels
Bibliographic record
Abstract
The results presented here highlight the extremely useful nature of ultra-short peptides as building blocks in the development of smart multicomponent supramolecular devices. A facile bottom-up strategy for the synthesis of a small library of stimuli-responsive smart organogelators has been proposed based on the predictive self-assembly of ultra-short peptides. More importantly, the narcissistic self-sorting of the gelators has been evaluated as a simple method for the efficient co-assembly of a donor-acceptor dual-component gel, allowing the investigation of possible future applications of similar systems in the development of a supramolecular photo-conversion device. Interestingly, it was observed that the self-organization of the components can lead to highly ordered systems in which discrimination between compatible and non-compatible building blocks directs the effective organization of the chromophores and gives rise to the formation of an excited-state complex with exciplex-like emission. The current report may prove important in the development of organogel-based multicomponent smart 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 | 0.001 |
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".