The Strong Influence of Structure Polymorphism on the Conductivity of Peptide Fibrils
Bibliographic record
Abstract
Peptide fibril nanostructures have been advocated as components of future biotechnology and nanotechnology devices. However, the ability to exploit the fibril functionality for applications, such as catalysis or electron transfer, depends on the formation of well-defined architectures. Fibrils made of peptides substituted with aromatic groups are described presenting efficient electron delocalization. Peptide self-assembly under various conditions produced polymorphic fibril products presenting distinctly different conductivities. This process is driven by a collective set of hydrogen bonding, electrostatic, and π-stacking interactions, and as a result it can be directed towards formation of a distinct polymorph by using the medium to enhance specific interactions rather than the others. This method facilitates the detailed characterization of different polymorphs, and allows specific conditions to be established that lead to the polymorph with the highest conductivity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 teacher head, 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".