The power of fluorescence excitation–emission matrix (EEM) spectroscopy in the identification and characterization of complex mixtures of fluorescent silver clusters
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 it