Biomimetic Porphyrin Aggregation for Developing Novel Phase Change Photonic Materials
Bibliographic record
Abstract
Porphyrins are aromatic, organic, light-absorbing molecules that occur abundantly in nature, especially in the form of molecular self-assemblies. Mimicking such highly efficient self-assembled systems has a vast potential in a variety of applications, from sensing, photomedicine to nanomedicine. Our recent discovery of porphysome provides a glimpse of this potential as the self-assembly of porphyrin-lipid building blocks enables intrinsic multimodal properties including photothermal/photoacoustic (intact state), photodynamic/fluorescent (disrupted state), and PET and MRI (metal chelating of porphyrin building blocks). To broaden its utility, we are looking for ways to create phase change porphysomes that could be developed into photonic molecular sensors that are capable of detecting environmental stimuli through alterations of optical absorption. Photosynthetic organisms have evolved ingenious strategies to optimize light absorption through nanoscale ordered dye aggregation. Learning from these nature’s self-assembly principles, we have recently succeeded in making the first phase change porphyrin nanoparticle, a tuneable, reversible and stimuli-responsive photoacoustic nanoswitch based on the change in aggregation-induced large absorption shift. Using this new photonic material, we non-invasively determined a localized temperature change in vivo, relevant for monitoring thermal therapies of solid tumors. Similar strategies may be applied alongside photoacoustic imaging, to detect other stimuli such as pH and enzymatic activity.
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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".