Synthesis of surface enhanced Raman scattering active magnetic nanoparticles for cell labeling and sorting
Bibliographic record
Abstract
Here we report the synthesis of the magnetic nanocomposite nanoparticles with Fe3O4 core and silver shell for cell imaging and separation. When the magnetic nanoparticles are decorated with surface enhanced Raman scattering (SERS) active molecules, they can be used for cell separation with unique optical signature. In this experiment, commercially available superparamagnetic nanoparticles (fluidMAG) with 50 nm diameter were used as the core. The shell layer was produced by the reduction of the silver salts. As a result of the reduction, nanocomposite magnetic nanoparticles with 60 nm diameter were obtained. To create unique SERS patterns for multiplexing, the surfaces of the nanoparticles were further modified with chloro-, bromo-, or fluorobenzenethiol. When these nanoparticles were incubated with 3T3 cells, it was found that the nanoparticles were located around the nucleus in the cytoplasm.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".