Comprehensive Screen of Metal Oxide Nanoparticles for DNA Adsorption, Fluorescence Quenching, and Anion Discrimination
Bibliographic record
Abstract
Although DNA has been quite successful in metal cation detection, anion detectioin remains challenging because of the charge repulsion. Metal oxides represent a very important class of materials, and different oxides might interact with anions differently. In this work, a comprehensive screen of common metal oxide nanoparticles (MONPs) was carried out for their ability to adsorb DNA, quench fluorescence, and release adsorbed DNA in the presence of target anions. A total of 19 MONPs were studied, including Al2O3, CeO2, CoO, Co3O4, Cr2O3, Fe2O3, Fe3O4, In2O3, ITO, Mn2O3, NiO, SiO2, SnO2, a-TiO2 (anatase), r-TiO2 (rutile), WO3, Y2O3, ZnO, ZrO2. These MONPs have different DNA adsorption affinity. Some adsorb DNA without quenching the fluorescence, while others strongly quench adsorbed fluorophores. They also display different affinity toward anions probed by DNA desorption. Finally, CeO2, Fe3O4, and ZnO were used to form a sensor array to discriminate phosphate, arsenate, and arsenite from the rest using linear discriminant analysis. This study not only provides a solution for anion discrimination using DNA as a signaling molecule but also provides insights into the interface of metal oxides and DNA.
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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 | 0.001 | 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".