Semiconductive and Biocompatible Nanofibrils from the Self-Assembly of Amyloid π-Conjugated Peptides
Bibliographic record
Abstract
Owing to their capacity to self-assemble into organized nanostructures, amyloid polypeptides can serve as scaffolds for the design of biocompatible semiconductive materials. Herein, symmetric and asymmetric amyloid π-conjugated peptides were prepared through condensation of perylene diimide (PDI) with a natural amyloidogenic sequence derived from the islet amyloid polypeptide. These PDI-bioconjugates assembled into long and linear nanofilaments in aqueous solution, which were characterized by a cross-β-sheet quaternary organization. Current-voltage curves exhibited a clear signature of semiconductors, whereas the cellular assays revealed cytocompatibility and potential application in fluorescence microscopy. Although the incorporation of a single amyloid peptide appeared sufficient to drive the self-assembly into organized fibrils, the incorporation of two peptide sequences at the PDI's imide positions significantly enhanced the conductivity of nanofibril-based films. Overall, this study exposes a novel strategy based on amyloidogenic peptide to guide the self-assembly of π-conjugated systems into robust, biocompatible, and optoelectronic nanofilaments.
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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) | 0.000 | 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 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".