Low‐Molecular‐Weight Gelators as Dual‐Responsive Chemosensors for the Naked‐Eye Detection of Mercury(II) and Copper(II) Ions and Molecular Logic Gates
Bibliographic record
Abstract
Abstract Three quinoline‐based Low Molecular Weight Gelators (LMWG's), containing benzimidazole and long alkyl chain moieties, with excellent gelation abilities have been designed and synthesized. The gel properties were studied by IR and NMR spectroscopies, scanning electron microscopy (SEM) and rheological measurements. The gelators showed excellent ability as efficient colorimetric and fluorescent sensors for detection of Hg 2+ and Cu 2+ ions in aqueous media. The supramolecular gel was selectively transformed into solution in the presence of Hg 2+ ion alongside complete quenching of its fluorescence intensity. In the presence of Cu 2+ ion, the gelator exhibited a significant color change from white to dark pink and its fluorescence intensity was substantially reduced in gel‐gel state. Other competing ions such as Mn 2+ , Zn 2+ , Pb 2+ , Cd 2+ , Mg 2+ , Ni 2+ and Al 3+ , induced no change under the same conditions. The gel function as molecular logic gate was also investigated and found that, the gelators implement the functions of INH and NOT gates with water and Hg 2+ ion as inputs and gel formation and fluorescence intensity as outputs.
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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".