Adsorption of Phosphate and Polyphosphate on Nanoceria Probed by DNA Oligonucleotides
Bibliographic record
Abstract
Phosphate-containing molecules exist in many forms in biology and the environment, and their interaction with metal oxides is an important aspect of their chemistry and biochemistry. In this work, phosphates with different degrees of polymerization (e.g., orthophosphate, pyrophosphate (PPi), sodium triphosphate (STPP), sodium trimetaphosphate (STMP), and polyphosphate with 25 phosphate units) and phosphates with one or two capping groups were studied. CeO 2 nanoparticles (nanoceria) were used as a model metal oxide. DNA is also a polyphosphate, and a fluorescently labeled DNA oligonucleotide was mixed with nanoceria. These phosphate species were individually added to displace the adsorbed DNA. Longer phosphate chains were more efficient when each molecule was used at the same molar concentration, whereas PPi and STPP were most efficient at the same total phosphorus atom concentration. By capping the phosphate with organic groups, the affinity was significantly decreased. Isothermal titration calorimetry (ITC) was also performed to quantitatively measure thermodynamic parameters. Although STMP was very slow at displacing DNA, it was still adsorbed very strongly by nanoceria from ITC, indicating kinetic effects likely due to its ring structure. This observation allowed us to use the DNA as a probe to study the hydrolysis of STMP to form STPP. In summary, this study provides a systematic understanding of phosphate species interacting with metal oxides, and interestingly, it demonstrates an analytical application as well.
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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) | 0.000 | 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) | 0.001 | 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".