Graphene Quantum Dot Bearing Liquid Droplets for Ultrasensitive Fluorescence-Based Detection of Nitroaromatics
Bibliographic record
Abstract
Sensing and detecting nitroaromatics (NAs) are essential for environmental, health, and safety reasons. Graphene quantum dots (GQDs) respond to the presence of NAs by a well-understood fluorescence quenching mechanism. However, despite the relative simplicity of fluorescence-based sensing, the limit of detection (LoD) can compare unfavorably with other methods. Here, we show that the LoD for sensors based on GQDs can be lowered by orders of magnitude using a droplet-based analyte partitioning effect. While previous efforts have attempted to improve the intrinsic GQD sensitivity via surface functionalization and size control, we show that a major improvement can be attained by changing from a bulk solution to droplet-based sensing of 2,4-dinitrotoluene and nitrobenzene. Moreover, the method is compatible with sensing from an aqueous solvent and has broader implications for many fluorescence-quenching-based sensing strategies that could benefit from partition-related enhancements.
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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 | 0.001 | 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 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".