Adsorption Promoted Aggregation-Induced Emission Showing Strong Dye Lateral Interactions
Bibliographic record
Abstract
Aggregation-induced emission (AIE) is a powerful method to produce fluorescence for a diverse range of applications. While most previous work induced aggregation by change of solvent, ionic strength, pH, or self-assembly, we herein explored adsorption-induced aggregation using 4,4′-(hydrazine-1,2-diylidene bis(methanylylidene)) bis(3-hydroxybenzoic acid) (HDBB) as an AIE luminogen. HDBB is known to aggregate with AIE at low pH but not at neutral pH, and its aggregation facilitates excited state intramolecular proton transfer for enhanced emission. Using a nonquenching nanomaterial, Y 2 O 3 nanoparticles, HDBB showed sevenfold fluorescence increase at pH 7.0. Fluorescence lifetime showed that HDBB was in the aggregated state in the presence of Y 2 O 3 . For comparison, a fluorescent porphyrin compound showed that adsorption caused quenching after mixing with Y 2 O 3, whereas other dyes such as fluorescein, calcein, and rhodamine B failed to be adsorbed by Y 2 O 3 . Adsorption did not follow a Langmuir isotherm, but it showed strong lateral HDBB interactions because adsorption was only achieved with a high concentration of HDBB. Adsorption was inhibited by salt and by phosphate, indicating the importance of electrostatic and metal-binding interactions. Comparisons were made with other nanomaterials, where graphene oxide and CeO 2 quenched HDBB and a cationic liposome also enhanced its emission, although with a less red-shifted peak wavelength. This study provides a simple method to induce aggregation of an AIE dye and its aggregation in turn-enhanced adsorption.
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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 | 0.001 |
| 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".