Photoinduced Electron Transfer in Perylene‐TiO<sub>2</sub> Nanoassemblies
Bibliographic record
Abstract
The photosensitization effect of three perylene dye derivatives on titanium dioxide nanoparticles (TiO2 NPs) has been investigated. The dyes used, 1,7-dibromoperylene-3,4,9,10-tetracarboxy dianhydride (1), 1,7-dipyrrolidinylperylene-3,4,9,10-tetracarboxy dianhydride (2) and 1,7-bis(4-tert-butylphenyloxy)perylene-3,4,9,10-tetracarboxy dianhydride (3) have in common bisanhydride groups that convert into TiO2 binding groups upon hydrolysis. The different substituents on the bay position of the dyes enable tuning of their redox properties to yield significantly different driving forces for photoinduced electron transfer (PeT). Recently developed TiO2 NPs having a small average size and a narrow distribution (4 ± 1 nm) are used in this work to prepare the dye-TiO2 systems under study. Whereas successful sensitization was obtained with 1 and 2 as evidenced by steady-state spectral shifts and transient absorption results, no evidence for the attachment of 3 to TiO2 was observed. The comparison of the rates of PeT (kPeT ) for 1- and 2-TiO2 systems studied in this work with those obtained for previously reported analogous systems, having TiO2 NPs covered by a surfactant layer (Hernandez et al. [2012] J. Phys. Chem. B., 117, 4568-4581), indicates that kPeT for the former systems is slower than that for the later. These results are interpreted in terms of the different energy values of the conduction band edge in each system.
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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".