Bibliographic record
Abstract
Abstract DNAzymes are catalytic DNA strands with interesting features such as high stability, versatile activity, and programmability. Interfacing DNAzymes with nanomaterials has boosted their function to achieve biosensing, intracellular imaging, smart materials, cleavage of viral/cancer RNA, and enhancing substrate specificity. This review starts with the introduction of a few commonly used DNAzymes for RNA cleavage, DNA cleavage, and peroxidation. The interactions of DNA and DNAzymes with various inorganic surfaces including gold, metal oxides, carbon‐based nanomaterials, metal–organic frameworks, and hydrogels are then discussed. DNAzymes can be adsorbed, covalently linked, or entrapped in these nanomaterials. After that, representative examples of applications are reviewed with an emphasis on the DNA/nanomaterials’ interfaces and fundamental chemical interactions. These examples include using nanomaterials for adsorbing DNAzymes and fluorescence quenching, producing a color change, assisting DNAzymes entering cells, supplying extra metal ions, and for molecularly imprinting target molecules. Finally, some key challenges in the field are discussed, and future research opportunities addressing these challenges are proposed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
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".