Photophysics of J-Aggregating Porphyrin-Lipid Photosensitizers in Liposomes: Impact of Lipid Saturation
Bibliographic record
Abstract
Porphyrin aggregates have attractive photophysical properties for phototherapy and optical imaging, including quenched photosensitization, efficient photothermal conversion, and unique absorption spectra. Although hydrophobic porphyrin photosensitizers have long been encapsulated into liposomes for drug delivery, little is known about the membrane properties of liposomes with large amphiphilic porphyrin compositions. In this paper, a porphyrin-lipid conjugate was incorporated into liposomes formed of saturated or unsaturated lipids to study the membrane composition-dependent formation of highly ordered porphyrin J-aggregates and disordered aggregates. Porphyrin-lipid readily phase-separates in saturated membranes, forming J-aggregates that are destabilized during the ripple phase below the main thermal transition. Porphyrin-lipid J-aggregates are photostable with a photothermal efficiency of 54 ± 6%, comparable to gold. Even at high porphyrin-lipid compositions, porphyrin J-aggregates coexist with a minority population of disordered aggregates, which are photodynamically active despite being fluorescently quenched. For photothermal applications, liposome formulations that encourage porphyrin-lipid phase separation should be explored for maximum J-aggregation.
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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".