Chitosan nanogels‐mediated <scp>AIE</scp> self‐assembly of copper nanoclusters for highly sensitive detection of the anticancer drug methotrexate
Bibliographic record
Abstract
Abstract In this study, a facile and fast aqueous‐phase synthetic method for preparing chitosan‐based fluorescent nanogels (CS) by exploiting the aggregation‐induced emission (AIE) property of copper nanoclusters (Cu NCs) is proposed. Owing to the spatial confinement provided by the crosslinking network structure in chitosan‐based nanogels and the electrostatic interaction between positively charged chitosan nanogels and negatively charged Cu NCs, the fluorescent intensity of the as‐prepared CS‐Cu NCs was increased by approximately 17‐fold compared with that of Cu NCs. The fluorescence quantum yield is increased by more than four times; the as‐prepared CS‐Cu NCs exhibited a quantum yield of 64.12%. Additionally, CS‐Cu NCs exhibited significantly improved stability in aqueous solution, including excellent oxidation resistance, high anti‐salt stability, good thermal stability, and enhanced capacity of anti‐photobleaching. These properties provide a fundamental guarantee for the application of Cu NCs with AIE property in the biosensor and bioimaging. Upon the addition of the anticancer drug methotrexate (MTX) to CS‐Cu NCs, their fluorescent intensity was markedly quenched. Based on the observed fluorescence‐quenching phenomenon of CS‐Cu NCs induced by MTX, a novel fluorescence quenching nanoprobe was designed for detecting the labeling amount percentage of commercially available methotrexate tablets. The experimental results validated that our proposed nanoprobe exhibits a wider dynamic linear range and excellent accuracy with a low limit of detection (LOD) of 4.16 μM, thereby expanding the potential application of chitosan‐based nanogels encapsulating metal nanoclusters presenting AIE property in pharmaceutical quality control.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".