Exploring DNA Nanostructures as Surface Engineering Techniques for Optimizing Nucleic Acid Biosensor Performance
Bibliographic record
Abstract
Surface modification of nucleic acid-based electrochemical biosensors has been at the forefront of research since their inception. Effective modification ensures the optimization of the sensitivity, specificity, and stability of modern biosensors. Recent advances in DNA nanotechnology have enabled the development of novel electrochemical biosensor interfaces with precise assembly and high biocompatibility. In this review, we explore three strategies for enhancing biosensor performance: the integration of tetrahedral DNA nanostructures (TDNs), self-assembled monolayers (SAMs), and DNA-based hydrogels. TDNs offer well-defined geometry and controlled spatial presentation of capture probes, significantly reducing background noise and improving target accessibility. SAMs provide a robust and tunable platform for anchoring these nanostructures, enabling reproducible and chemically stable interfaces. DNA hydrogels serve as a responsive and flexible scaffold capable of signal amplification and analyte retention. These surface architectures enhance sensitivity and minimize non-specific adsorption (NSA). We discuss recent applications and experimental outcomes, highlighting how each component is driving the next generation of nucleic acid-based biosensors.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".