Recent Advances in Thermally Activated Delayed Fluorescent Materials in Type II Photodynamic Therapy
Bibliographic record
Abstract
Abstract Photodynamic therapy (PDT) represents a novel, dual‐stage cancer treatment approach that combines light energy and photosensitizers to destroy cancerous and precancerous cells through the generation of radicals (Type I) or singlet oxygen (Type II). Since the early 2010s, PDT has advanced significantly, with the focus shifting toward the exploration of molecules capable of thermally activated delayed fluorescence (TADF) as viable alternatives to traditional metallic complexes and organometallic compounds for producing the necessary active species. TADF molecules exhibit higher energy conversion efficiency, long‐lived triplet excitons, tunable photophysical properties, and a small singlet‐triplet energy gap, facilitating efficient intersystem crossing and enhanced singlet oxygen generation. As metal‐free luminophores, they offer benefits such as reduced health risks, high structural flexibility, and biocompatibility, which can significantly enhance PDT treatment efficacy. Notably, in 2019, a pivotal shift occurred, with researchers concentrating their efforts on identifying and investing in potential molecules specifically for Type II PDT applications. This review presents the innovative use of materials characterized by closely spaced S1 and T1 orbitals, crucial for the efficient generation of singlet oxygen in PDT. Exploring these materials opens new avenues for enhancing the efficacy and specificity of PDT, offering promising for future cancer treatments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".