Design of Triads for Probing the Direct Through Space Energy Transfers in Closely Spaced Assemblies
Bibliographic record
Abstract
Using a selective stepwise Suzuki cross-coupling reaction, two trimers built on three different chromophores were prepared. These trimers exhibit a D(^)A1-A2 structure where the donor D (octa-β-alkyl zinc(II)porphyrin either as diethylhexamethyl, 10a, or tetraethyltetramethyl, 10b, derivatives) through space transfers the S1 energy to two different acceptors, di(4-ethylbenzene) zinc(II)porphyrin (A1; acceptor 1) placed cofacial with D, and the corresponding free base (A2; acceptor 2), which is meso-meso-linked with A1. This structure design allows for the possibility of comparing two series of assemblies, 9a,b (D(^)A1) with 10a,b (D(^)Â1-A2), for the evaluation of the S1 energy transfer for the global process D*→A2 in the trimers. From the comparison of the decays of the fluorescence of D, the rates for through space energy transfer, kET for 10a,b (kET ≈ 6.4 × 10(9) (10a), 5.9 × 10(9) s(-1) (10b)), and those for the corresponding cofacial D(^)A1 systems, 9a,b, (kET ≈ 5.0 × 10(9) (9a), 4.7 × 10(9) s(-1) (9b)), provide an estimate for kET for the direct through space D*→A2 process (i.e., kET(D(^)A1-A2) - kET(D(^)A1) = kET(D*→A2) ∼ 1 × 10(9) s(-1)). This channel of relaxation represents ∼15% of kET for D*→A1.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".