Theoretical Study of Polaron Formation in Poly(G)−Poly(C) Cations
Bibliographic record
Abstract
Polaron formation in poly(G)-poly(C) cations is investigated with density functional theory (DFT) and molecular mechanics (MM) employing a two-layer ONIOM method. In these calculations, the high layer, composed of all complementary base pairs, is treated by a DFT method, while the low layer, which includes the sugar-phosphate backbone, counterions and water molecules, is described by the AMBER force field. The high layer is the model system in which the charge transfer takes place. According to our calculations, three or four guanines move in a paddle-like fashion when an electron is removed from the neutral model system. In the cation model system, about 80% of the charge is delocalized onto the guanine residues, and the remaining charge is delocalized onto the cytosine residues. This happens because guanine has a lower ionization potential (IP) than cytosine. The counterions and water molecules in the low layer are important in the geometry optimization. The optimized geometry of the model system is closer to the standard B-form structure when counterions and water molecules are included than when they are omitted. Comparison of the optimized neutral and cationic model systems reveals a polaron in poly(G)-poly(C) cations extending from the first to the third guanine. It is demonstrated that the position of counterions and the number of surrounding water molecules can affect polaron formation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".