Length-Dependent Diblock DNA with Poly-cytosine (Poly-C) as High-Affinity Anchors on Graphene Oxide
Bibliographic record
Abstract
DNA-functionalized graphene oxide (GO) is a popular system for biosensor development and directed materials assembly. Compared to covalent attachment, simple physisorption of DNA has been more popular, and a DNA sequence with a strong affinity on GO is highly desirable. Recently, we found that poly-cytosine (poly-C) DNA can strongly adsorb on many common nanomaterials, including GO. To identify an optimal length of poly-C DNA, we herein designed a series of diblock DNA sequences containing between 0 and 30 cytosines. The displacement of a random sequenced DNA by poly-C DNA was demonstrated, confirming the desired diblock structure on GO with the poly-C block anchoring on the surface and the other block available for hybridization. The adsorption density of poly-C containing DNA did not vary much as the length of the poly-C block increased, suggesting the conformation of the anchoring DNA on the GO was quite independent of the DNA length. With a longer poly-C block, the efficiency of surface hybridization of the other block increased, while nonspecific adsorption of noncomplementary DNA was inhibited more. Compared to poly-adenine (poly-A)-containing DNAs, which were previously used for the same purpose, poly-C DNA adsorption is more stable. Using four types of 15-mer DNA homopolymers as the intended anchoring sequences, the C 15 DNA had the best hybridization efficiency. This work has suggested the optimal length for the poly-C block to be 15-mer or longer, and it has provided interesting insights into the DNA/GO biointerface.
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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".