Fluorescent Nile blue‐functionalized poly (<i>N</i>‐isopropylacrylamide) microgels responsive to temperature and polyamines
Bibliographic record
Abstract
Abstract Fluorescent poly( N ‐isopropylacrylamide‐ co ‐Nile blue) (pNIPAm‐ co ‐NB) microgels were synthesized that exhibited fluorescence intensity changes in a water temperature‐dependent fashion. NB is well known to exhibit fluorescence intensity that depends on the hydrophobicity of the environment, while pNIPAm‐based microgels are well known to transition from swollen (hydrophilic) to collapsed (relatively hydrophobic) at temperatures greater than 32 °C; hence, we attribute the above behavior to the hydrophobicity changes of the microgels with increasing temperature. This phenomenon is ultimately due to NB dimers (relatively quenched fluorescence) being broken in the hydrophobic environment of the microgels leading to relatively enhanced fluorescence. We went on to show that the introduction of cucurbit[7]uril (CB[7]) into the pNIPAm‐ co ‐NB microgels enhanced their fluorescence allowing them to be used for polyamine (e.g., spermine [SPM]) detection. Specifically, CB[7] forms a host–guest interaction with NB in the microgels, which prevents NB dimerization and enhances their fluorescence. When SPM is present, it forms a host–guest complex that is favored over the CB[7]‐NB host–guest interaction, which frees the NB for dimerization and leads to fluorescence quenching. As a result, we could generate an SPM sensor capable of SPM detection down to ~0.5 µmol/L in complicated matrixes such as serum and urine.
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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) | 0.000 | 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".