Distinction of Individual Lanthanide Ions with a DNAzyme Beacon Array
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
Developing chemical probes to distinguish each lanthanide ion is a long-standing challenge. Aside from its analytical applications, solving this problem will also enhance our knowledge in metal ligand design. Using in vitro selection, we previously reported four RNA-cleaving DNAzymes, each with a different activity trend cross the lanthanide series. We herein performed another eight in vitro selection experiments using each and every lanthanide from La 3+ to Tb 3+ but excluding the radioactive Pm 3+ . A new DNAzyme named Gd2b was identified and characterized. By labeling this DNAzyme with a fluorophore/quencher pair to create a catalytic beacon, a detection limit of 14 nM Gd 3+ was achieved. With the same beacon design, all the five lanthanide-specific DNAzymes were used together to form a sensor array. Each lanthanide ion produces a unique response pattern with these five sensors, allowing a pattern-recognition-based linear discriminant analysis (LDA) algorithm to be applied, where separation was achieved between lanthanides and nonlanthanides, light and heavy lanthanides, and for the most part, each lanthanide. These lanthanide-specific DNA molecules are useful for understanding lanthanide coordination chemistry, designing hybrid materials, and developing related analytical probes.
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