A green route to prepare fluorescent and absorbent nano-hybrid hydrogel for water detection
Bibliographic record
Abstract
Abstract An environment-friendly fluorescent nano-hybrid hydrogel has been synthesized successfully, from cellulose nanocrystal (CNC), acrylic acid (AA) and phosphorescent Eu2+/Dy3+doped SrAl2O4via free radical polymerization. The hydrogel network matrix fixed Eu2+/Dy3+doped SrAl2O4nanoparticles by polymer chains with coordinate bonds that prevented particles from aggregating and quenching in water. The fluorescent nano-hybrid hydrogel exhibited extremely high water absorption of which the swelling ratio in distilled water and NaCl salt solution were respectively of 323.35 g/g and 32.65 g/g. Furthermore, the hydrogel displayed excellent water retention property that can keep half of the moisture even exposed to 80 °C for 210 min. Besides, the hydrogel had bright green fluorescence under the sunlight or ultraviolet excitation, and the fluorescence intensity was up to 125477 after swelling 50 times in water. The time-resolved photoluminescence (TRPL) afterglow decay examination showed that the fluorescent emission persisted for 4 h after hydrogels excited at 368 nm wavelength UV-light for 10 min. The fluorescence intensity behaved significant linear relationship with the swelling ratio. As a result, these hydrogels were considered as promising candidates for the preparation of stable and sensitive sensor materials in green water detection.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".