Kinetic and Affinity Profiling Rare Earth Metals Using a DNA Aptamer
Bibliographic record
Abstract
Rare earth elements (REEs) are widely used in various high-tech industries. Developing affinity ligands that can detect and distinguish REEs is at the forefront of analytical chemistry. It is also interesting to understand the limits of natural biomolecules for the recognition of REEs. In this study, Sc 3+ was used as a target for the isolation of DNA aptamers, and an aptamer named Sc-1 was obtained. Using a thioflavin T (ThT) fluorescence assay, Sc-1 bound only to REEs, but not other metal ions. Additionally, the binding of Sc-1 to Sc 3+ exhibited slow kinetics, and the binding complex resisted dissociation by EDTA. Furthermore, Sc-1 displayed varying binding kinetics with trivalent lanthanide ions, allowing for the discrimination of 17 REEs into three major groups: (1) La 3+, Ce 3+, Pr 3+, Nd 3+, Sm 3+, Eu 3+, and Gd 3+; (2) Tb 3+, Dy 3+, Ho 3+, Er 3+, Tm 3+, Yb 3+, Lu 3+, and Y 3+; and (3) Sc 3+ . NMR spectroscopy confirmed binding-induced conformational changes in the aptamer. Using the fluorescence strand-displacement method, the true K d of the aptamer was measured to range from 0.6 to 258.5 nM for the REE ions, and it showed effective detection of Sc 3+ in real samples.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".