Comprehensive Screen of Metal Oxide Nanoparticles for DNA Adsorption, Fluorescence Quenching, and Anion Discrimination
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 it