Manipulating Active Sites of 2D Metal–Organic Framework Nanosheets with Fluorescent Materials for Enhanced Colorimetric and Fluorescent Ammonia Sensing
Bibliographic record
Abstract
Abstract 2D metal–organic frameworks (MOFs) offer high surface area and unique accessibility to active adsorption sites making them appealing for gas sensing applications. 2D MOFs‐based sensors are gaining traction for detecting hazardous flu‐gases such as ammonia selectively at low concentrations. Fluorescent and colorimetric sensing are promising techniques offering high sensitivity, selectivity, and rapid response in simple applications. In this work, Zn‐BTC is synthesized as 2D‐MOFs nanosheet with approximate thickness of 2.52 nm via a fast, facile, direct synthesis technique. The introduction of 8‐hydroxyquinoline during synthesis forms fluorescent compounds with zinc (ZnQ) which is encapsulated and decorated onto Zn‐BTC. Inherent charges on ZnQ lead to the agglomeration of multiple 2D‐flakes forming ZnQ@Zn‐BTC multi‐flaked nano‐discs. The synthesized material shows visible color change upon exposure to ammonia from white to ivory. In addition, selective fluorescence quenching is observed under ultraviolet illumination (λex = 365 nm) when ZnQ@Zn‐BTC is exposed to ammonia. The limit of detection reaches 0.27 ppm as a dried film for gaseous sensing and 60.8 nm in liquid phase fluorescence quenching, respectively. The observed high sensitivity and selectivity are attributed to the manipulation of active sites of 2D‐MOFs nanosheet with ZnQ. Functionalization also limits the degradation and breakdown of ZnQ@Zn‐BTC.
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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) | 0.001 | 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".