Best Practices to Directly Assess Heterogeneous Singlet Oxygen Photosensitization by Phosphorescence
Bibliographic record
Abstract
Abstract Literature proves that the direct detection of 1O2(1Δg) at the solid gas interface is systematically performed from its phosphorescence using high intensity excitation sources (i.e., lasers), which lead to quasi‐ubiquitous chemical problems, such as sensitizer degradation, and photophysical counter‐active issues such as ultrafast exciton migration, singlet‐singlet and triplet‐triplet annihilation, and thermally activated delayed fluorescence mediated by 1O2(1Δg). To avoid these inconveniences, low excitation intensity is required but leads to serious analytical challenges. The best practices to reliably detect 1O2(1Δg) phosphorescence at various interfaces using a standard excitation source and near‐IR detector. The two main practices consist in a gas purging test for reliable identification of 1O2(1Δg), and in a particularly fine optimization of the angle made by excitation beam versus substrate plane. These practices are applied to porphyrin sensitizers H2TPP and ZnTPP, either neat or physiosorbed on glass, quartz, paper and hospital bandages, graphene oxide (GO), and embedded inside electrospun polystyrene fibers and spin coated poly(methyl methacrylate) films. Porphyrin‐based metal‐organic framework PCN‐224, freshly activated, is also examined.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".