Dissecting the effect of anions on Hg2+ detection using a FRET based DNA probe
Bibliographic record
Abstract
Many biosensors have been developed to detect Hg(2+) using thymine-rich DNA. While sensor response to various cations is often studied to demonstrate selectivity, the effect of anions has been largely overlooked. Anions may compete with DNA for metal binding and thus produce a false negative result. Anions cannot be added alone; the cation part of a salt may cause DNA compaction and other effects, obscuring the role of anions. We find that the sensitivity of a FRET-based Hg(2+) probe is independent of Na(+) concentration. Therefore, by using various sodium salts, any change in sensitivity can be attributed solely to the effect of anions. Halide salts, sulfides, and amines are strong inhibitors; anions containing oxo or hydroxyl groups (e.g. nitrate, sulfate, phosphate, carbonate, acetate, and citrate) do not interfere with Hg(2+) detection even at 100 mM concentration. Mercury hydrolysis and its diffusion into polypropylene containers can also strongly affect the detection results. We conclude that thymine-rich DNA should be useful for Hg(2+) detection in many environmental water samples.
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.
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 | 0.001 | 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".