Adsorption and Desorption of DNA on Graphene Oxide Studied by Fluorescently Labeled Oligonucleotides
Bibliographic record
Abstract
Being the newest member of the carbon materials family, graphene possesses many unique physical properties resulting is a wide range of applications. Recently, it was discovered that graphene oxide can effectively adsorb DNA, and at the same time, it can completely quench adsorbed fluorophores. These properties make it possible to prepare DNA-based optical sensors using graphene oxide. While practical analytical applications are being demonstrated, the fundamental understanding of binding between graphene oxide and DNA in solution received relatively less attention. In this work, we report that the adsorption of 12-, 18-, 24-, and 36-mer single-stranded DNA on graphene oxide is affected by several factors. For example, shorter DNAs are adsorbed more rapidly and bind more tightly to the surface of graphene. The adsorption is favored by a lower pH and a higher ionic strength. The presence of organic solvents such as ethanol can either increase or decrease adsorption depending on the ionic strength of the solution. By adding the cDNA, close to 100% desorption of the absorbed DNA on graphene can be achieved. On the other hand, if temperature is increased, only a small percentage of DNA is desorbed. Further, the adsorbed DNA can also be exchanged by free DNA in solution. These findings are important for further understanding of the interactions between DNA and graphene and for the optimization of DNA and graphene-based devices and sensors.
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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".