Graphene oxide as a photocatalytic nuclease mimicking nanozyme for DNA cleavage
Bibliographic record
Abstract
Developing nanomaterial-based enzyme mimics for DNA cleavage is an interesting challenge and it has many potential applications. Single-layered graphene oxide (GO) is an excellent platform for DNA adsorption. In addition, GO has been employed for photosensitized generation of reactive oxygen species (ROS). Herein, we demonstrate that GO sheets could cleave DNA as a nuclease mimicking nanozyme in the presence of UV or blue light. For various DNA sequences and lengths, well-defined product bands were observed along with photobleaching of the fluorophore label on the DNA. Different from previously reported GO cleavage of DNA, our method did not require metal ions such as Cu 2+ . Fluorescence spectroscopy suggested a high adsorption affinity between GO and DNA. For comparison, although zero-dimensional fluorescent carbon dots (C-dots) had higher photosensitivity in terms of producing ROS, their cleavage activity was much lower and only smeared cleavage products were observed, indicating that the ROS acted on the DNA in solution. Based on the results, GO behaved like a classic heterogeneous catalyst following substrate adsorption, reaction, and product desorption steps. This simple strategy may help in the design of new nanozymes by introducing light.
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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 | 0.001 | 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 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".