Adsorption of DNA Oligonucleotides by Titanium Dioxide Nanoparticles
Bibliographic record
Abstract
Titanium dioxide (TiO2) or titania shows great promise in detoxification and drug delivery. To reach its full potential, it is important to interface TiO2 with biomolecules to harness their molecular recognition function. To this end, DNA attachment is an important topic. Previous work has mainly focused on long double-stranded DNA or single nucleotides. For biosensor development and targeted drug delivery, it is more important to use single-stranded oligonucleotides. Herein, the interaction between fluorescently labeled oligonucleotides and TiO2 nanoparticles is reported. The point of zero charge (PZC) of TiO2 is around 6 in water or acetate buffer; therefore, the particles are positively charged at lower pH. However, if in phosphate or citrate buffer, the particles are negatively charged, even at pH ∼2, suggesting strong adsorption of buffer anions. DNA adsorption takes place mainly via the phosphate backbone, although the bases might also have moderate contributions. Peptide nucleic acids (PNAs) with an amide backbone cannot be adsorbed. DNA adsorption is strongly affected by inorganic anions, where phosphate and citrate can strongly inhibit DNA adsorption. DNA adsorption is promoted by adding salt or lowering pH. DNA adsorption is accompanied with fluorescence quenching, and double-stranded DNA showed reduced quenching, allowing for the detection of DNA using TiO2 nanoparticles.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".