Investigation of the Impact of Hydrogen Bonding Degree in Long Single-Stranded DNA (ssDNA) Generated with Dual Rolling Circle Amplification (RCA) on the Preparation and Performance of DNA Hydrogels
Bibliographic record
Abstract
DNA hydrogels have gained significant attention in recent years as one of the most promising functional polymer materials. To broaden their applications, it is critical to develop efficient methods for the preparation of bulk-scale DNA hydrogels with adjustable mechanical properties. Herein, we introduce a straightforward and efficient molecular design approach to producing physically pure DNA hydrogel and controlling its mechanical properties by adjusting the degree of hydrogen bonding in ultralong single-stranded DNA (ssDNA) precursors, which were generated using a dual rolling circle amplification (RCA)-based strategy. The effect of hydrogen bonding degree on the performance of DNA hydrogels was thoroughly investigated by analyzing the preparation process, morphology, rheology, microstructure, and entrapment efficiency of the hydrogels for Au nanoparticles (AuNPs)-BSA. Our results demonstrate that DNA hydrogels can be formed at 25 °C with simple vortex mixing in less than 10 s. The experimental results also indicate that a higher degree of hydrogen bonding in the precursor DNA resulted in stronger internal interaction forces, a more complex internal network of the hydrogel, a denser hydrogel, improved mechanical properties, and enhanced entrapment efficiency. This study intuitively demonstrates the effect of hydrogen bonding on the preparation and properties of DNA hydrogels. The method and results presented in this study are of great significance for improving the synthesis efficiency and economy of DNA hydrogels, enhancing and adjusting the overall quality and performance of the hydrogel, and expanding the application field of DNA hydrogels.
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 | 0.001 |
| 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 | 0.000 | 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".