Rationally Designed Nucleobase and Nucleotide Coordinated Nanoparticles for Selective DNA Adsorption and Detection
Bibliographic record
Abstract
Nanomaterials for DNA adsorption are useful for sequence-specific DNA detection. Current materials for DNA adsorption employ electrostatic attraction, hydrophobic interaction, or π-π stacking, none of which can achieve sequence specificity. Specificity might be improved by involving hydrogen bonding and metal coordination. In this work, a diverse range of nucleobase/nucleotide (adenine, adenosine, adenosine 5'-triphosphate (ATP), adenosine 5'-monophosphate (AMP), and guanosine 5'-triphosphate (GTP)) coordinated materials containing various metal ions (Au(III), Ag(I), Ce(III), Gd(III), and Tb(III)) are prepared. In most cases, nanoparticles are formed. These materials have different surface charges, and positively charged particles only show nonspecific DNA adsorption. Negatively charged materials give different adsorption kinetics for different DNA sequences, where complementary DNA homopolymers are adsorbed faster than other sequences. Therefore, the bases in the coordinated materials can still form base pairs with the DNA. The adsorption strength is mainly controlled by the metal ions, where Au shows the strongest adsorption while lanthanides are weaker. These materials can be used as sensors for DNA detection and can also deliver DNA into cells with no detectable toxicity. By tuning the nanoparticle formulation, enhanced detection can be achieved. This study is an important step toward rational design of materials to achieve specific interactions between biomolecules and synthetic nanoparticle surfaces.
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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".