Chiral‐induced highly efficient NIR–photothermal conversion of perylene diimide@silica nanocapsules for photothermal therapy
Bibliographic record
Abstract
Abstract Photothermal agents (PTAs) with ultra‐high photothermal conversion efficiency (PCE) activated upon near‐infrared (NIR) laser irradiation can heat up and destroy tumor cells under low‐intensity laser excitation to allow safe and efficient tumor therapy. Herein, an organic PTA with an outstanding PCE of 89.6% is developed from rationally designed perylene diimide (PDI) with electron‐donating cyclohexylamine moiety at the bay‐positions of its skeleton and chiral phenethylamine (PEA) moiety at its N terminals, termed here PEAPDI. The strong intermolecular interaction between the PDI skeletons induced by PEA together with the intramolecular charge transfer from cyclohexylamine to PDI skeleton severely quenches the fluorescence emission from PEAPDI and significantly enhances its NIR absorption, resulting in super NIR–photothermal conversion. PEAPDI molecules are subsequently encapsulated within silica nanocapsules (SNCs), creating PEAPDI@SNC. Characterized by its small hydrodynamic diameter, monodispersity, high PDI encapsulation efficiency, colloidal stability, and biocompatibility, PEAPDI@SNC exhibits prolonged blood circulation and enhanced permeability and retention effect, enabling targeted accumulation at the tumor site. An in vivo study using a 4T1 tumor–bearing mice model illustrates the agent's potent tumor ablation capability without side effects at low dosage under NIR laser irradiation (808 nm). The findings demonstrate PEAPDI@SNC's significant potential as a PTA for tumor treatment.
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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 | 0.000 |
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".