Path to Improvement of the Photocatalytic Performances of Porphyrin-Based MOFs
Bibliographic record
Abstract
Porphyrin-based metal-organic frameworks (PMOFs) have attracted substantial attention due to their rich photophysical1 and photocatalytic properties.2 A recent survey3 indicates that the size of the nanoparticle, in the nano- or micro-scale, does not influence the rate of the photocatalytic conversion nor the quantity produced. This led to the conclusion that most efficient photoactive sites are located at the surface pores, suggesting that the bulk of the material appears to be geometrically quasi-useless. Moreover, because of the large optical density of a solid sample, absorption of photon by the core is rather limited. This situation is remedied by decreasing the size of the particles (nanoparticles, NPs). However, this still does not explain why the photoconversion rates are size independent of the NPs.3 One major factor that is different form one study to another in this survey is the PMOF motif. Exciton migration has been considered a factor that influences the efficiency of the photocatalysis4,5 or the photosensitization of singlet oxygen, 1O2(g),6 up to a factor of about 10 folds. In this latter study, the Forster theory was applied to rationalize the exciton migration and a clear relationship between path of exciton migration, relative rates of hopping, and relative quantity of 1O2(g) photosensitized, exists. The presentation explains in a broad sense how this relationship works using several examples issues from our laboratory. References: 1-P.D.Harvey “Porphyrin-basedMetal- and Covalent-OrganicFrameworksas Heterogeneous Nanosized Photocatalysts in Organic Synthesis”. J. Mat. Chem. C, 2021, 9, 16885-16910 2-S.M.Sajjadinezhad,L.Boivin,K.Bouarab,P.D.Harvey“Photophysical Properties andPhotonicApplications ofPorphyrin-Based MOFs” Coord. Chem. Reviews, 2024, 510, 215794 3-P. Asselin, P. D.Harvey“Visible-Light-DrivenProductionofSolar FuelsCatalyzed byNano-sized Porphyrin-Based MOFs and COFs: A Review” ACS Appl. Nano Mat., 2022, 5, 6055-6082 4-Choi, S. ; Jung, W.-J.; Park, K.; Kim, S.-Y. ; Baeg, J.-O. ; Kim, C.H.; Son,H.-J. ;Pac, C.; Kang, S.O. “Rapid Exciton Migration and Amplified Funneling Effects of Multi-Porphyrin Arrays in a Re(I)/Porphyrinic MOF Hybrid Photocatalytic CO2 Reduction” ACS Appl. Mat. & Interfaces 2021, 13, 2710-2722 5-Stanley, P. M.; Hemmer, K.; Hegelmann, M.; Schulz, A.; Park, M.; Elsner, M.; Cokoja, M.; Warnan, J. “Topology- and Wavelength-governed CO2 Reduction Photocatalysis in Molecular Catalyst-Metal-Organic-Framework Assemblies” Chem. Science 2022, 13, 12164-12174 6-Asselin, P.; Schlachter, A.; Fortin, D.; Karsenti, P.-L.; Harvey, P. D. “Structural Influence on Exciton Migration and 1O2 Photosensitization in Porphyrinic Metal-Organic Coordination Networks” Chem. Mat. 2022, 34, 7242-7255
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".