Highly selective and sensitive fluorescent zeolitic imidazole frameworks sensor for nitroaromatic explosive detection
Bibliographic record
Abstract
Nitroaromatic explosives, such as 2-4-6 trinitrotoluene (TNT) are dangerous materials that pose safety and environmental risks. Even though many sensors have been reported for the detection of nitroaromatic explosives, a facile, rapid, cost-effective sensor is still sought-after in the field. Here we demonstrate a facile and rapid method to synthesize a fluorescent metal-organic framework for the highly selective and sensitive detection of nitroaromatic explosives. Zeolitic imidazole framework-8 (ZIF-8) is synthesized and enhanced with fluorescent 8-hydroxyquinoline zinc (ZnQ). The synthesized material shows visible colour changes upon exposure to TNT from ivory to light red. In addition, fluorescence quenching is noted under UV illumination when the ZnQ@ZIF-8 is exposed to TNT. The ZnQ@ZIF-8-coated paper sensors show the highest fluorescence quenching at an emission wavelength of 455 nm with TNT concentration as low as 1 ppm. Therefore, the proposed strategy not only offers a fast and convenient protocol for selective detection of TNT but also offers great potential in practical applications, especially for airport/railway security inspection and prevention of terrorist attacks.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".