Supramolecularly Assembled Ratiometric Fluorescent Sensory Nanosystem for “Traffic Light”-Type Lead Ion or pH Sensing
Bibliographic record
Abstract
The combination of functional nucleic acids and nanomaterials enables the continuous development of hybrid nanosystems that have found wide applications including chemo/biosensing. Herein, we report the supramolecular assembly of a “sesame biscuit”-like superstructural nanosystem based on aptamer, quantum dot (QD), and graphene oxide (GO), and its diverse applications in Pb 2+ and pH sensing. The nanosystem was assembled via adsorbing silica-encapsulated green-emitting QD onto the edge of GO by ionic interaction, followed by absorbing aptamer-modified red-emitting QD onto the GO surface via the π-stacking interaction. The nanosystem showed the characteristic of the nonquenched green fluorescence due to silica encapsulation and quenched red fluorescence owing to nanomaterial surface energy transfer. The nanosystem responded to Pb 2+ /pH in ratiometric fluorescence: the red fluorescence varied upon analyte-driven conformational changes of the aptamer, whereas the green one remained constant. Under optimized conditions, the nanosystem was demonstrated to be capable of quantifying Pb 2+ with a detection limit of 11.7 pM, as well as pH with a sensing resolution of 0.1 pH unit. More importantly, the ratiometric nanosystem facilitated visualization of analytes in a distinct “traffic light” manner, which was exemplified by semiquantification of exogenous Pb 2+ in living cells. To demonstrate practicality, fluorescent test strips were fabricated by immobilizing the nanosystem on paper. The fluorescent test strips displayed traffic light-type fluorescence color changes, with the capacity for on-site, naked-eye detection of Pb 2+ in real samples, as well as point-of-care pH testing in routine urinalysis.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".