Detection and Quantitation of Heavy Metal Ions on Bona Fide DVDs Using DNA Molecular Beacon Probes
Bibliographic record
Abstract
A sensitive and cost-effective method for the simultaneous quantitation of trace amounts of Hg(2+) and Pb(2+) in real-world samples has been developed using DNA molecular beacon probes bound to bona fide digital video discs (DVDs). With specially designed T-rich or G-rich loop sequences, the detection is based on the strong T-Hg(2+)-T coordination chemistry of Hg(2+) and the formation of G-quadruplexes induced by Pb(2+), respectively. In particular, the presence of metal cations leads to hairpin opening and exposure of the terminal biotin moiety for binding nanogold-streptavidin conjugates. The recognition signal was subsequently enhanced by gold nanoparticle-promoted silver deposition, which leads to quantifiable digital signals upon reading with a standard computer drive. This method exhibits a wide response range and low detection limits for both Hg(2+) and Pb(2+). In addition, the quantitative determination of heavy metals in food products (e.g., rice samples) has been demonstrated and the method compares favorably with other optical sensors developed recently.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".