DNA attachment to polymeric, soft and quantum materials: mechanisms and applications
Bibliographic record
Abstract
Polymeric and soft materials offer excellent biocompatibility, a high capacity for loading of guest molecules, and cost-effectiveness. Additionally, recent studies on nanodiamonds as quantum materials have revealed interesting applications at ambient temperatures. The attachment of DNA oligonucleotides to these materials enables molecular recognition, directed assembly and targeting capabilities, offering unique advantages for biomedical, analytical and environmental applications. In this article, the mechanisms of DNA adsorption to various metal-free materials, including polydopamine (PDA), hydrogels, microplastics, cellulose crystals, nanodiamonds, and carbon quantum dots are reviewed. Key interactions involved in these systems include π-π stacking, hydrogen bonding, hydrophobic interactions, and metal bridging. We highlight how material properties such as surface charge, functional groups, and wettability influence DNA adsorption and release. Stimuli-responsive systems, such as pH-switchable PDA and thermoresponsive hydrogels, allow controlled DNA adsorption and release. Furthermore, sequence-specific aptamers developed for microplastics and cellulose are discussed, demonstrating the potential for selective DNA binding to nanomaterials. Finally, applications including fluorescence-based biosensors, intracellular delivery, high-density DNA storage, and surface probing are presented. Remaining challenges and future directions are also discussed to guide further advancements in this emerging field.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".