Assembly of Macrocycle Dye Derivatives into Particles for Fluorescence and Photoacoustic Applications
Bibliographic record
Abstract
Optical imaging is a rapidly progressing medical technique that can benefit from the development of new and improved optical imaging agents suitable for use in vivo. However, the molecular rules detailing what optical agents can be processed and encapsulated into in vivo presentable forms are not known. We here present the screening of series of highly hydrophobic porphyrin, phthalocyanine, and naphthalocyanine dye macrocycles through a self-assembling Flash NanoPrecipitation process to form a series of water dispersible dye nanoparticles (NPs). Ten out of 19 tested dyes could be formed into poly(ethylene glycol) coated nanoparticles 60-150 nm in size, and these results shed insight on dye structural criteria that are required to permit dye assembly into NPs. Dye NPs display a diverse range of absorbance profiles with absorbance maxima within the NIR region, and have absorbance that can be tuned by varying dye choice or by doping bulking materials in the NP core. Particle properties such as dye core load and the compositions of co-core dopants were varied, and subsequent effects on photoacoustic and fluorescence signal intensities were measured. These results provide guidelines for designing NPs optimized for photoacoustic imaging and NPs optimized for fluorescence imaging. This work provides important details for dye NP engineering, and expands the optical imaging tools available for use.
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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 | 0.000 | 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".