Poly‐Cytosine Deoxyribonucleic Acid Strongly Anchoring on Graphene Oxide Due to Flexible Backbone Phosphate Interactions
Bibliographic record
Abstract
Abstract Finding DNA sequences that can strongly adsorb on various nanomaterials is critically important for preparing bioconjugates, biosensors, and drug delivery. Poly‐cytosine (poly‐C) DNA is found to have stronger affinity compared to other DNA sequences of the same length on various nanomaterials ranging from graphene oxide (GO), MoS2, to many metal oxides and phosphates. In this work, the authors aim to understand the reason for such high affinity by varying pH and DNA sequence along with conducting molecular dynamics (MD) simulations using GO as a model surface. Poly‐C DNA adsorbs stronger only at neutral or basic pH, while its adsorption at acidic pH is weaker than other DNA homopolymers. The DNA sequence is further varied by inserting thymine into poly‐C DNA and by varying thymine/cytosine ratios, all confirming that a folded i‐motif structure is detrimental for adsorption. Using MD simulations, the authors reveal that the stronger adsorption of poly‐C DNA at neutral pH is due to more contributions from the phosphate backbone hydrogen bonding with GO surface relating to the flexibility of the DNA. Poly‐C DNA also uses its phosphate backbone to interact with metal oxide and phosphate nanoparticles, and this phosphate backbone interaction can unify all these observations.
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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".