Fluorescence turn on amine detection in a cationic covalent organic framework
Bibliographic record
Abstract
Abstract Ionic covalent organic frameworks (iCOFs) are new examples of porous materials and have shown great potential for various applications. When functionalized with suitable emission sites, guest uptake via the ionic moieties of iCOFs can cause a significant change in luminescence, making them excellent candidates for chemosensors. In here, we present a luminescence sensor in the form of an ionic covalent organic framework (TGH + •PD) composed of guanidinium and phenanthroline moieties for the detection of ammonia and primary aliphatic amines. TGH + •PD exhibits strong emission enhancement in the presence of selective primary amines due to the suppression of intramolecular charge transfer (ICT) with an ultra-low detection limit of 1.2 × 10 ‒7 M for ammonia. The presence of ionic moieties makes TGH + •PD highly dispersible in water, while deprotonation of the guanidinium moiety by amines restricts its ICT process and signals their presence by enhanced fluorescence emission. The presence of ordered pore walls introduces size selectivity among analyte molecules, and the iCOF has been successfully used to monitor meat products that release biogenic amine vapors upon decomposition due to improper storage.
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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".