Metallopolymer−Peptide Conjugates: Synthesis and Self-Assembly of Polyferrocenylsilane Graft and Block Copolymers Containing a β-Sheet Forming Gly-Ala-Gly-Ala Tetrapeptide Segment
Bibliographic record
Abstract
We describe the synthesis and self-assembly of two beta-sheet forming metallopolymer-peptide conjugates. The ability of the oligotetrapeptide sequence Gly-Ala-Gly-Ala (GAGA) to form antiparallel beta-sheets was retained in PFS-b-AGAG (PFS = polyferrocenylsilane) and PFS-g-AGAG conjugates with block and graft architectures, respectively. In the solid state, DSC experiments suggest a phase separation between the peptide and PFS domains. In toluene, PFS-b-AGAG interestingly forms a fibrous network which consists of a core containing the self-assembled antiparallel beta-sheet peptide and a corona of organometallic PFS. The self-assembly of the peptide into antiparallel beta-sheets is the driving force for the fiber formation, whereas PFS prevents uncontrolled lateral aggregation of the fibers. The use of an oligopeptide to self-assemble an otherwise random coiled organometallic polymer may be a useful strategy to enhance nanostructure formation. In the cases described here, the conjugates may be used to create nanopatterned ceramics, and the redox properties of the resulting supramolecular aggregates are of significant interest.
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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 | 0.000 |
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".