Effects of Polyethylene Glycol on DNA Adsorption and Hybridization on Gold Nanoparticles and Graphene Oxide
Bibliographic record
Abstract
Understanding the interface between DNA and nanomaterials is crucial for rational design and optimization of biosensors and drug delivery systems. For detection and delivery into cells, where high concentrations of cellular proteins are present, another layer of complexity is added. In this context, we employ polyethylene glycol (PEG) as a model polymer to mimic the excluded volume effect of cellular proteins and to test its effects on DNA adsorption and hybridization on gold nanoparticles (AuNPs) and graphene oxide (GO), both of which show great promise for designing intracellular biosensors and drug delivery systems. We show that PEG 20000 (e.g., 4%) accelerates DNA hybridization to DNA-functionalized AuNPs by 50-100%, but this enhanced hybridization kinetics has not been observed with free DNA. Therefore, this rate enhancement is attributed to the surface blocking effect by PEG instead of the macromolecular crowding effect. On the other hand, DNA adsorption on citrate-capped AuNP surfaces is impeded even in the presence of a trace level (i.e., parts per billion) of PEG, confirming PEG competes with DNA for surface binding sites. Additional insights have been obtained by studying the adsorption of a thiolated DNA and a peptide nucleic acid. In these cases, the steric effects of PEG to impede adsorption are observed. Similar observations have also been made with GO. Therefore, PEG may be used as an effective blocking agent for both hydrophilic AuNP and for GO that also contains hydrophobic domains.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 teacher head, 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".