Metal-Enhanced Hg<sup>2+</sup>-Responsive Fluorescent Nanoprobes: From Morphological Design to Application to Natural Waters
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Metal-enhanced fluorescence (MEF) is a powerful tool in the design of sensitive chemical sensors by improving brightness and photostability of target-responsive fluorophores. Compounding these advantages with the modest hardware requirements of fluorescence sensing compared to that of centralized elemental analysis instruments, thus expanding the use of MEF to the detection of low-level inorganic pollutants, is a compelling aspiration. Among the latter, monitoring mercury in the environment, where some of its species disseminate through the food chain and, in time, to humans, has elicited a broad research effort toward the development of Hg 2+ -responsive fluorescent sensors. Herein, a Hg 2+ -sensitive MEF-enabled probe was conceived by grafting a Hg 2+ -responsive fluorescein derivative to concentric [email protected] 2 NPs, where the metallic core enhances fluorescence emission of molecular probes embedded in a surrounding silica shell. Time-resolved fluorescence measurements showed that the fluorophore’s excited-state lifetime decreases from 3.9 ns in a solid, coreless silica sphere to 0.4 ns in the core–shell nanoprobe, granting the dye a better resistance to photobleaching. The Ag-core system showed a sizable improvement in the limit of detection at 2 nM (0.4 ppb) compared to 50 nM (10 ppb) in silica-only colloids, and its effectiveness for natural water analysis was demonstrated. Overall, the reported nanoarchitecture hints at the potential of MEF for heavy metal detection by fluorescence detection.
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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) | 0.000 | 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 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".