Nanostructure‐Driven Indocyanine Green Dimerization Generates Ultra‐Stable Phototheranostics Nanoparticles
Bibliographic record
Abstract
Indocyanine green (ICG) is the only near-infrared (NIR) dye approved for clinical use. Despite its versatility in photonic applications and potential for photothermal therapy, its photobleaching hinders its application. Here we discovered a nanostructure of dimeric ICG (Nano-dICG) generated by using ICG to stabilize nanoemulsions, after which ICG enabled complete dimerization on the nanoemulsion shell, followed by J-aggregation of ICG-dimer, resulting in a narrow, red-shifted (780 nm→894 nm) and intense (≈2-fold) absorbance. Compared to ICG, Nano-dICG demonstrated superior photothermal conversion (2-fold higher), significantly reduced photodegradation (-9.6 % vs. -46.3 %), and undiminished photothermal effect (7 vs. 2 cycles) under repeated irradiations, in addition to excellent colloidal and structural stabilities. Following intravenous injection, Nano-dICG enabled real-time tracking of its delivery to mouse tumors within 24 h by photoacoustic imaging at NIR wavelength (890 nm) distinct from the endogenous signal to guide effective photothermal therapy. The unprecedented finding of nanostructure-driven ICG dimerization leads to an ultra-stable phototheranostic platform.
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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) | 0.000 | 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 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".