Bibliographic record
Abstract
Nanomedicine takes advantage of new, medically useful properties that arise from nanoscale control of material composition and structure. This thesis investigates a nanoscale photophysical phenomenon called J-aggregation within the context of porphyrin-lipid nanomedicine. Porphyrins are endogenous chromophores medically used as photosensitizers and optical imaging agents. Conjugating porphyrins to lipids enables their self-assembly into lipid nanoparticles. Interactions between porphyrins in the nanoparticle lipid membrane changes their photophysical properties. J-aggregation occurs when porphyrins are aligned head-to-tail and results in a dramatic red-shift in the absorption spectrum. Because J-aggregation relies on nanoscale dye–dye ordering it is sensitive to external stimuli that impact membrane structure, including temperature and interactions with biological species. In Chapter 1, porphyrin-lipid nanomedicine is first contextualized within the nanomedicine field. In Chapter 2, a background is provided on the mechanisms that alter the photophysical properties of dyes in nanomedicines and porphyrin J-aggregation is specifically reviewed. Chapter 3 examines the nanostructure-dependent photophysical properties of porphyrin-lipid aggregates in lipid membranes. Chapter 4 investigates the optical stability of J-aggregating porphyrin-lipid nanoparticles during exposure to serum proteins. Chapter 5 explores the potential utility of these J-nanoparticles for phototherapy and optical imaging applications. Lastly, Chapter 6 presents a discussion of new knowledge gained and potential future directions. From these fundamental studies, an assessment is provided of whether the unique nanostructure-dependent photophysical properties of J-nanoparticles are also potentially medically useful.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".