Kinetic Discrimination of Metal Ions Using DNA for Highly Sensitive and Selective Cr<sup>3+</sup> Detection
Bibliographic record
Abstract
Most metal sensors are designed for a strong binding affinity toward target metal ions, and the underlying principle relies on binding thermodynamics. The kinetic aspect of binding, however, was rarely explored for sensing. In this work, the binding kinetics of 19 common or toxic metal ions are compared based on a fluorescence quenching assay using DNA oligonucleotides as ligands. Among these metals, Cr 3+ shows uniquely slow fluorescence quenching kinetics, and the quenched fluorescence cannot be recovered by EDTA or sulfide. Most other metals quenched fluorescence instantaneously and can be fully recovered by these metal chelators. Various factors such as DNA sequence and length, chelating agent, pH, and fluorophore type were studied to understand the binding mechanism, leading to a unique two-stage binding model for Cr 3+ . This system has a wide dynamic range of up to 50 μM Cr 3+ and a low limit of detection of 80 nM. It is also useful for measuring Cr 3+ in lake water. This work proposes a new metal sensor design by monitoring binding kinetics with Cr 3+ being a primary example.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".