A water-soluble fluorescent chemosensor based on Asp functionalized naphthalimide for successive detection Fe<sup>3+</sup> and H<sub>2</sub>PO<sub>4</sub><sup>−</sup>
Bibliographic record
Abstract
The selective recognition of target ions in water is very important and the development of novel water-soluble chemosensor is still an intriguing challenge. Herein, a novel water-soluble fluorescent sensor based on aspartic acid (Asp) functionalized 1,8-naphthalimide derivative (Asp-NI) has been designed and synthesized. The sensor Asp-NI could dissolve in water and successively detect Fe3+ and H2PO4− in water solution with high selectivity and sensitivity. The detection limits are 4.97 × 10−7 mol/L for Fe3+ and 5.27 × 10−6 mol/L for H2PO4−. Other coexistent competitive metal ions (Hg2+, Ag+, Ca2+, Cu2+, Co2+, Ni2+, Cd2+, Pb2+, Zn2+, Cr3+, and Mg2+) showed no interference in the Fe3+ detection process. The sensor Asp-NI could act as a Fe3+ and H2PO4− controlled “On–Off–On” fluorescent switch. More interestingly, the Fe3+ induced fluorescence quenching process could be totally reversed by the addition of H2PO4−, this “On–Off–On” switching process could be repeated several times with little fluorescence loss. Notably, the actual usage of sensor Asp-NI was further demonstrated by test kits.
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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) | 0.001 | 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 | 0.001 | 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".