DNA-Functionalized Gold Nanoparticles in Macromolecularly Crowded Polymer Solutions
Bibliographic record
Abstract
DNA-functionalized gold nanoparticles (AuNPs) are one of the most commonly used reagents in nanobiotechnology. They are important not only for practical applications in analytical chemistry and drug delivery, but also for fundamental understanding of nanoscience. For biological samples such as blood serum or for intracellular applications, the effects of crowded cellular proteins and nucleic acids need to be considered. The thermodynamic effect of crowding is to induce nanoparticle aggregation. But before such aggregation can take place, there might also be a depletion repulsive barrier. Polyethylene glycol (PEG) is one of the most frequently used polymers to mimic the crowded cellular environment. We show herein that while DNA-functionalized AuNPs are very stable in buffer (e.g., no PEG) and citrate-capped AuNPs are very stable in PEG, DNA-functionalized AuNPs are unstable in PEG and are easily aggregated. Although such aggregation in PEG is mediated by DNA, no sharp melting transition typical for DNA-linked AuNPs is observed. We attribute this broad melting to depletion force instead of DNA base pairing. The effects of PEG molecular weight, concentration and temperature have been studied in detail and we also find an interesting PEG phase separation and AuNP partition into the water-rich phase at high temperature.
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 | 0.000 | 0.001 |
| 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".