The power of fluorescence excitation–emission matrix (EEM) spectroscopy in the identification and characterization of complex mixtures of fluorescent silver clusters
Bibliographic record
Abstract
Silver and gold clusters have received a lot of recent attention for their use in biomedical imaging. However, crude solutions of clusters are often complex mixtures, leading to discrepancies in their identification and characterization; important factors in determining their utility in biological applications. In the present study, silver clusters were separated for analysis using reverse-phase high performance liquid chromatography, which has previously been implemented in the efficient separation of gold clusters. Using fluorescence excitation-emission matrix (EEM) spectroscopy, we have demonstrated that a certain family of glutathione-protected silver clusters, previously thought to be one optically distinct species, is better described as a complex mixture of at least three distinct silver cluster species, each possessing unique optical properties. Based on these findings, EEM spectroscopy can be implemented as a powerful technique for determining the purity of complex mixtures, especially when other techniques, including mass spectrometry, fail to provide adequate characterization of a given material.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".