Optimisation of Dyes@SWCNT Raman Nanoprobes
Bibliographic record
Abstract
Single-Walled Carbon Nanotubes (SCWNTs) have attracted a lot of attention in biomedical fields. Their easily functionalised surface and ability to encapsulate different materials make them interesting not only for imaging, but also for other applications, such as drug delivery and cell targeting. This work concerns specifically the use of SWCNTs to fabricate Raman nanoprobes for bio-imaging. These nanoprobes are composed of dyes encapsulated inside the SWCNTs and the nanotubes are grafted with anti-bodies functionalized on the outer surface. First, we aim to optimize the encapsulation process so that the dye/SWCNTs nanohybrid gives a strong, uniform and reproductible signal that is easily detectable by Raman imaging. This is done by varying the parameters of the liquid-phase encapsulation process and crosschecking the results by Raman imaging. We also work to optimize the stability of the nanohybrid in biological media. This parameter is important to determine the best conditions for antibody attachment and cell/tissue interactions. Using different biological buffers, we study the dispersion of the assemblies with Dynamic Light Scattering (DLS) and their stability with Electrophoretic Light Scattering (ELS). The results of the analysis on the nanohybrids show an average hydrodynamic radius of 160 nm and a Zeta potential at -45 mV in aqueous media, proving that the stability of the short nanohybrids in water is favourable to the development of nanoprobes.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".