Synthesis and Photophysical Characterization of Axially Functionalized Silicon(IV) Naphthalocyanines with Phototheranostic Potential
Bibliographic record
Abstract
Naphthalocyanines are emerging as highly promising photosensitizers, owing to their intensive absorption ( ε > 1 × 10 5 M −1 cm −1 ) beyond 800 nm, but their poor solubility in biological media has significantly limited their clinical translation. In this study, the synthesis and detailed photophysical characterization of silicon naphthalocyanines (SiNcs) functionalized with structurally diverse axial ligands designed to enhance solubility and evaluate photophysical properties for theranostic applications are reported. Through an efficient synthetic approach, a library of SiNc derivatives are generated and systematically evaluated for their Q‐band absorption shift, absolute fluorescence quantum yields, lifetime, radiative decay rate, and singlet oxygen generation. The lead compound, SiNc 3 , is encapsulated in poly(ethylene glycol)‐b‐poly( ε ‐caprolactone) (PEG‐PCL) micelles to improve biocompatibility and in vivo delivery. Axial ligand modifications are found to influence aggregation behavior within the nanoformulation, enabling SiNc 3‐PP to retain a strong and sharp absorption peak at 810 nm and exhibit high photothermal conversion efficiency (62.1%). SiNc 3‐PP demonstrates strong photoacoustic imaging contrast at 810 nm, and upon near‐infrared irradiation in tumor‐bearing mice, induced rapid tumor heating up to 54 °C within 5 min, resulting in significant tumor growth inhibition. These findings underscore the pivotal role of axial ligand engineering in tuning SiNc behavior and highlight their potential as theranostic agents integrating photoacoustic imaging and photothermal therapy.
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) | 0.000 | 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".