A sensitive naked eye probe based on spiropyran for exactness sensing of F <sup>−</sup> ions in aqueous
Bibliographic record
Abstract
Most areas have long been affected by endemic fluorosis, which can lead to fluorosis bone disease and cardiovascular damage. The detection and treatment of fluoridated water has been of great concern. In this work, the sensors SP-1 and SP-2 were synthesized from naphthylamine and pyreneamine with carboxy-modified spiropyran via amidation reaction. The photoisomerization of spiropyran molecules under alternating ultraviolet/visible light irradiation resulted in changes in the conjugation system, which amplifies the combination between F − and the amide bonds of SP-1 and SP-2 molecules, enabling rapid and naked-eye detection of F − . Simultaneously, the impact of two distinct conjugated rings, pyridineamine and naphthaleneamine, on spiropyran detection of F − was investigated. Based on results from the UV spectrum, 1 HNMR, and DFT theoretical calculation, it was determined that both sensor SP-1 and SP-2 can detect F − with a binding ratio of 1:1. The detection limits for sensor SP-1 are 0.978 µM for F − , and SP-2 are 0.173 µM for F − . The detection limit of SP-2, with a larger chromophore than SP-1, is lower and exhibits a more pronounced phenomenon. This study holds significant practical implications for rapid anion detection in aqueous environments, visual recognition without the need for specialized equipment, and energy conservation.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".