Replication Data for: 2H-NMR as a practical tool for following MOF formation: a case study of UiO-66
Bibliographic record
Abstract
Abstract: Deuterium-NMR spectroscopy is the optimal inter-lab methodology to understand in-situ kinetics of metal-organic framework (MOF) formation. This method is facile, affordable, and can be used to isolate and monitor one reagent at a time by using one deuterated component with the remaining components having no deuterium present. Developing a mechanistic basis for MOF formation is critical for rapid development of new materials. This work utilizes 2H-NMR, by means of the spectrometer’s lock channel, to demonstrate how UiO 66 forms as a function of different modulators (acetic acid, benzoic acid, and hydrochloric acid). Monitoring the concentration of deuterated linker and the chemical shift and peak width of deuterated water over time unravels key elements of MOF formation. Paradoxically, conditions that would cause ligand to be consumed more slowly results in MOFs to appear more quickly and with fewer defects. This is due to the dissociative mechanism associated with the ZrIV-containing node.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.073 | 0.050 |
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".