A Complementary Silicon Quantum Dot-Enzyme Platform for the Selective Detection of Nitroaromatic Compounds: Explosives versus Nerve Agents
Bibliographic record
Abstract
To address the issue of poor selectivity in nanotechnology-driven, portable nitroaromatic sensors, we have coupled a ratiometric photoluminescent sensor based on silicon quantum dots and fluorescent proteins with a colorimetric, enzyme-based sensor. Together, the sensors allow differentiation of nitroaromatic compounds─specifically, distinguishing acetylcholinergic nerve agents from the explosive compounds explored herein. The combined system can detect 2,4,6-trinitrotoluene, 2,4-dinitrotoluene, and 4-nitrophenol with micromolar detection limits and affords subsequent differentiation from the nitro-containing nerve agent paraoxon. A prototype portable sensing device housing both technologies is presented and tested for achieving visual differentiation of the nitroaromatic explosives versus nerve agents. This demonstrates the advantage of merging elements of materials chemistry and biochemistry to devise customized sensors that can accurately identify hazardous chemical species.
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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 | 0.000 | 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 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".