Tuning Structural and Optical Properties of Porphyrin‐based Hydrogen‐Bonded Organic Frameworks by Metal Insertion
Bibliographic record
Abstract
Abstract Herein, a simple way of tuning the optical and structural properties of porphyrin‐based hydrogen‐bonded organic frameworks (HOFs) is reported. By inserting transition metal ions into the porphyrin cores of GTUB‐5 ( p ‐H 8 ‐TPPA (5,10,15,20‐Tetrakis[p‐phenylphosphonic acid] HOF), the authors show that it is possible to generate HOFs with different band gaps, photoluminescence (PL) life times, and textural properties. The band gaps of the resulting HOFs (viz., Cu‐, Ni‐, Pd‐, and Zn‐GTUB‐5) are measured by diffuse reflectance and PL spectroscopy, as well as calculated via DFT, and the PL lifetimes are measured. Across the series, the band gaps vary over a narrow range from 1.37 to 1.62 eV, while the PL lifetimes vary over a wide range from 2.3 to 83 ns. These differences ultimately arise from metal‐induced structural changes, viz., changes in the metal‐to‐nitrogen distances, number of hydrogen bonds, and pore volumes. DFT reveals that the band gaps of Cu‐, Zn‐, and Pd‐ GTUB‐5 are governed by highest occupied/lowest unoccupied crystal orbitals (HOCO/LUCO) composed of π‐ orbitals on the porphyrin linkers, while that of Ni‐GTUB‐5 is governed by a HOCO and LUCO composed of Ni dorbitals. Overall, our findings show that metal‐insertion can be used to optimize HOFs for optoelectronics and small‐molecule capture applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.003 | 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 teacher head, 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".