How Reproducible is the Synthesis of Zr–Porphyrin Metal–Organic Frameworks? An Interlaboratory Study
Bibliographic record
Abstract
Metal-organic frameworks (MOFs) are a rapidly growing class of materials that offer great promise in various applications. However, the synthesis remains challenging: for example, a range of crystal structures can often be accessed from the same building blocks, which complicates the phase selectivity. Likewise, the high sensitivity to slight changes in synthesis conditions may cause reproducibility issues. This is crucial, as it hampers the research and commercialization of affected MOFs. Here, it presents the first-ever interlaboratory study of the synthetic reproducibility of two Zr-porphyrin MOFs, PCN-222 and PCN-224, to investigate the scope of this problem. For PCN-222, only one sample out of ten was phase pure and of the correct symmetry, while for PCN-224, three are phase pure, although none of these show the spatial linker order characteristic of PCN-224. Instead, these samples resemble dPCN-224 (disordered PCN-224), which has recently been reported. The variability in thermal behavior, defect content, and surface area of the synthesised samples are also studied. The results have important ramifications for field of metal-organic frameworks and their crystallization, by highlighting the synthetic challenges associated with a multi-variable synthesis space and flat energy landscapes characteristic of MOFs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".