Best Practices for the Synthesis, Activation, and Characterization of Metal–Organic Frameworks
Bibliographic record
Abstract
Metal–organic frameworks (MOFs) are structurally diverse materials comprised of inorganic and organic components. As the rapidly expanding field of MOF research has demonstrated, these materials are being explored for a wide variety of potential applications. In this tutorial review, we give an overview of the current best practices associated with the synthesis, activation, and characterization of MOFs. Methods described include supercritical CO 2 activation, single crystal X-ray diffraction (XRD), powder X-ray diffraction (PXRD), nitrogen adsorption/desorption isotherms, surface area calculations, aqueous stability tests, scanning electron microscopy (SEM), inductively coupled plasma optical emission spectroscopy (ICP-OES), nuclear magnetic resonance spectroscopy (NMR), and diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS). A variety of different MOFs are presented to aid in the discussion of relevant techniques. In addition, some sections are accompanied by instructional videos to give further insight into the techniques, including tips, tricks, and suggestions only those at the bench could describe.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.006 |
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".