A fluorescence sensor based on quantum dots for the detection of mercury ions
Bibliographic record
Abstract
Mercury(II) ion (Hg2+) is one of the most widespread pollutants that poses a serious threat to public health and the environment. Research efforts on selective and sensitive detection of Hg2+ have therefore drawn considerable attention in recent years. Herein, we report a facile approach to detect Hg2+ based on quantum dot (QD)-based nanosensor. The two single-stranded DNA (ssDNA) used in this work are modified with biotin (ssDNA–biotin) and fluorescence black hole quencher BHQ2 (ssDNA–BHQ2). These two strands are complementary but with TTT-recognized base sequences for the Hg2+ to form a T–Hg2+–T complex. The biotin-modified ssDNA (ssDNA–biotin) is first bound to the streptavidin-modified QDs, forming a QDs/ssDNA–biotin assembly, which may be further hybridized with the ssDNA–BHQ2, producing a complex of QDs/ssDNA–biotin/ssDNA–BHQ2. The BHQ2 serves as an effective quencher of QDs with the QDs and BHQ2 in a proximity within the QDs/ssDNA–biotin/ssDNA–BHQ2 complex. The decrease of fluorescence intensity therefore serves as an indication of the presence of Hg2+. The fluorescence reduction is observed linearly correlated with the concentration of Hg2+ in the range of 1.0–20.0 nmol/L, with a detection limit at 0.87 nmol/L. The presented QD-based method is expected to provide a simple, rapid, and sensitive method for the detection of Hg2+ in environmental water 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".