Creating Order in Ultrastable Phosphonate Metal–Organic Frameworks via Isolable Hydrogen-Bonded Intermediates
Bibliographic record
Abstract
The stability presented by trivalent metal–organic frameworks (MOFs) makes them an attractive class of materials. With phosphonate-based ligands, crystallization is a challenge, as there are significantly more binding motifs that can be adopted due to the extra oxygen tether compared to carboxylate counterparts and the self-assembly processes are less reversible. Despite this, we have reported charge-assisted hydrogen-bonded metal–organic frameworks (HMOFs) consisting of [Cr(H 2 O) 6 ] 3+ and phosphonate ligands, which were crystallographically characterized. We sought to use these HMOFs as a crystalline intermediate to synthesize ordered Cr(III)-phosphonate MOFs. This can be done by dehydrating the HMOF to remove the aquo ligands around the Cr(III) center, forcing metal–phosphonate coordination. Herein, a new porous HMOF, H-CALF-50, is synthesized and then dehydrated to yield the MOF CALF-50. CALF-50 is ordered, although it is not single crystalline. It does, however, have exceptional stability, maintaining crystallinity and surface area after boiling in water for 3 weeks and soaking in 14.5 M H 3 PO 4 for 24 h and 9 M HCl for 72 h. Computational methods are used to study the HMOF to MOF transformation and give insight into the nature of the structure and the degree of heterogeneity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".