Truxene-to-Fluorenone Energy Transfer in a Robust Mesoporous Zn-MOF
Bibliographic record
Abstract
A new metal–organic framework (MOF; [Zn 4 O( hett ) 4/3 ( fluo ) 1/2 ( bdc ) 1/2 ] n; TFT-MOF ) constructed on chromophoric ligands 5,5′,10,10′,15,15′-hexaethyltruxene-2,7,12-triacetate ( hett ), 9-fluorenone-2,7-dicarboxylate ( fluo ), terephthalate ( bdc ), and the Zn 4 O node has been prepared and identified by powder X-ray diffraction. This luminescent MOF exhibits large mesoporous pores of 2.7 nm based on computer modeling using density functional theory (DFT) calculations. The steady-state and time-resolved fluorescence spectra and photophysical parameters of TFT-MOF have been investigated and compared with those of the free ligands and their basic chromophores. All in all, TFT-MOF exhibits particularly efficient singlet–singlet energy-transfer processes described as 1 ( hett )* → ( fluo ) and 1 ( bdc )* → ( fluo ), leading to fluorescence arising for the fluo lumophore operating only through Förster resonance energy transfer (FRET) with an efficiency of transfer of up to >95%. This experimental conclusion was corroborated by DFT and time-dependent DFT (TDDFT). For the 1 ( hett )* → ( fluo ) process, the approximated overall rate constant of energy transfer was evaluated to be at most 2.04 × 10 10 s –1 (using a Stern–Volmer approach of solution data and the relationship between distance and concentration). This process was analyzed using the Förster theory, where two intrapore energy transfer paths of center-to-center distances of 13 and 25 Å have been identified. TFT-MOF photosensitizes the formation of singlet oxygen ( 1 O 2 ( 1 Σ g )) as detected by its phosphorescence signal at 1275 nm.
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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) | 0.000 | 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".