Interfacing DNA and Polydopamine Nanoparticles and Its Applications
Bibliographic record
Abstract
Abstract Polydopamine (PDA) is polymerized from dopamine under oxidative and basic conditions. PDA is a biocompatible material with great versatility for coating various surfaces and it can also form nanoparticles. DNA oligonucleotides are highly stable, and they can recognize a diverse range of molecules from complementary nucleic acids, proteins to small molecules and metal ions. To enhance the molecular recognition function of PDA, it is interfaced with DNA and the conjugates have achieved a wide range of applications in biomedical and analytical science. In this review, the chemistry of some catecholamines, including dopamine and PDA, is first briefly introduced and variables in the PDA synthesis are highlighted. Strategies to promote DNA adsorption on PDA are then discussed including the use of low pH and polyvalent metal ions. In addition, covalent attachment of DNA to PDA can be achieved by using amino or thiol‐modified DNA, forming highly stable conjugates. The specific applications of PDA–DNA conjugate are also delineated, including DNA extraction, biosensing, intracellular delivery, and DNA origami. Finally, some problems in the field are discussed, along with a few future research opportunities.
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 | 0.000 |
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".