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 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 | 0.000 |
| 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) | 0.000 | 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".