Detection of nitroaromatics in the solid, solution, and vapor phases using silicon quantum dot sensors
Why this work is in the frame
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Bibliographic record
Abstract
Silicon quantum dots (Si-QDs) represent a well-known QD fluorophore that can emit throughout the visible spectrum depending on the interface structure and surface functional group. Detection of nitroaromatic compounds by monitoring the luminescence response of the sensor material (typically fluorescent polymers) currently forms the basis of new explosives sensing technologies. Freestanding silicon QDs may represent a benign alternative with a high degree of chemical and physical versatility. Here, we investigate dodecyl and amine-terminated Si-QD luminescence response to the presence of nitrobenzene and dinitrotoluene (DNT) in various solid, solution, and vapor forms. For dinitrotoluene vapor the 3σ detection limit was 6 ppb for monomer-terminated QDs. For nitroaromatics dissolved in toluene the detection limit was on the order of 400 nM, corresponding to ∼100 pg of material distributed over ∼1 cm(2) on the sensor surface. Solid traces of nitroaromatics were also easily detectable via a simple 'touch test'. The samples showed minimal interference effects from common contaminants such as water, ethanol, and acetonitrile. The sensor can be as simple and inexpensive as a small circle of filter paper dipped into a QD solution, with a single vial of QDs able to make hundreds of these sensors. Additionally, a trial fiber-optic sensor device was tested by applying the QDs to one end of a 2 × 2 fiber coupler and exposing them to controlled DNT vapor. Finally, the quenching mechanism was explored via luminescence dynamics measurements and is different for blue (amine) and red (dodecyl) fluorescent silicon QDs.
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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 it