Synthesizing Ag<sup>+</sup>: MgS, Ag<sup>+</sup>: Nb<sub>2</sub>S<sub>5</sub>, Sm<sup>3+</sup>: Y<sub>2</sub>S<sub>3</sub>, Sm<sup>3+</sup>:Er<sub>2</sub>S<sub>3</sub>, and Sm<sup>3+</sup>:ZrS<sub>2</sub> Compound Nanoparticles for Multicolor Fluorescence Imaging of Biotissues
Bibliographic record
Abstract
Abstract Development of the fluorophores whose fluorescence bands can be flexibly selected is of great interest for biotissue imaging. Compounds of Ag+:MgS, Ag+:Nb2S5, Sm3+:Y2S3, Sm3+:Er2S3, and Sm3+:ZrS2 were obtained through new chemical synthesis. They were characterized by X-ray photoelectron spectroscopy, X-ray diffraction spectroscopy, and transmission electron microscopy. They revealed polychromatic-photoluminescence spectra when excited by 280, 380, 480, 580, 680, and 785 nm light. Especially, near-infrared emission ranging from 800–1100 nm was found upon 785 nm light excitation. A band model was proposed to explain transitions responsible for the observed components of emission. Their broad fluorescence spectra cover from the ultraviolet to near-infrared spectral range. Their ability of emitting wide-range fluorescence was utilized for multicolor fluorescence imaging of biotissues, as demonstrated by pig-kidney tissue samples.
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.
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) | 0.001 | 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".