In situ encapsulation of ZrQ in UiO‐66 (Zr‐BDC) for pore size control to enhance detection of a nerve agent simulant dimethyl methyl phosphonate
Bibliographic record
Abstract
Chemical warfare agents are toxic chemicals that require rapid, easy‐to‐use, sensitive, and selective sensors to countermeasure. Simulants, such as dimethyl methyl phosphonate (DMMP), are used to test the effectiveness of sensors toward nerve agents. Metal organic frameworks (MOFs) offer large surface area and selective accessibility to active sites making them appealing for chemical sensing applications. In this work, we propose a fast, facile, direct synthesis method for manufacturing fluorescent MOFs with high sensitivity and selectivity. Zr‐BDC is synthesized with 1, 4‐benzenedicarboxylic acid (BDC) as an organic ligand and zirconium (Zr) metal. Fluorescent materials are then encapsulated in a novel and rapid in situ approach with strong solvents. X‐ray diffraction, UV–visible spectroscopy, Fourier‐transform infrared spectroscopy, and Raman spectroscopy are used to verify the successful formation of fluorescent MOFs. Compared to other methods, the gel synthesis method helps to control crystal growth leading to higher BET surface areas of ~1150 m2 g−1 for Zr‐BDC and 850 m2 g−1 for ZrQ@Zr‐BDC. Titration experiments show the sensitivity of the material to DMMP down to 8.3 nM with a highly linear response. Enhanced fluorescence and occupation of mesopores by ZrQ enable lower limit of detection than those of comparable works in literature. The encapsulation mechanism also prevents substantial defects that would otherwise lead to water adsorption.
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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 | 0.000 | 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 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".