<scp>DNA</scp> ‐Enabled Heavy Metal Detection in Water
Bibliographic record
Abstract
Abstract Detection of heavy metal contamination in the environment is an on‐going analytical challenge. In effort of developing portable biosensors, deoxyribonucleic acid (DNA)‐based designs have gained much attention for their high affinity and specificity to metals, stability, cost‐efficiency, ease of modification, and batch‐to‐batch reproducibility. Specific sequences of DNA aptamers and DNAzymes provide grounds for rational designs of fluorescent, colorimetric, and electrochemical detection methods. Aptamers exert only a binding function, while DNAzymes can use heavy metals to catalyze specific chemical and biological transformations. This article starts with a brief introduction of heavy metals and their interactions with DNA. Then DNA aptamers and DNAzymes are respectively reviewed from their in vitro selection, representative DNA sequences, and design of biosensors. For signal transduction, various fluorescent, colorimetric, and electrochemical examples are described. Finally, future perspectives are discussed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".