Dynamic Nuclear Polarization-enhanced NMR and Its Applications for the Structural Investigation of MOFs and COFs
Bibliographic record
Abstract
Dynamic nuclear polarization (DNP) is a cutting-edge technique designed to enhance NMR signal intensities and overcome the intrinsically low sensitivity of NMR spectroscopy. The DNP transfer of spin polarization from unpaired electrons to the surrounding nuclei results in signal enhancement by two to three orders of magnitude. In this chapter, we first provide an introduction to DNP, and then focus on the applications of DNP-enhanced NMR for structural characterization of metal–organic frameworks (MOFs) and covalent organic frameworks (COFs). The tremendous sensitivity improvements provided by DNP, along with the advent of high magnetic fields and NMR probes capable of fast and ultra-fast magic angle spinning (MAS) rates, now allow researchers to answer various questions regarding MOFs and COFs that cannot be addressed by conventional solid-state NMR spectroscopy or other characterization techniques. With the aid of DNP, very challenging NMR experiments have been proven to be feasible; DNP has been used to perform typically insensitive 27Al–13C 2D experiments, obtain ultra-wideline 195Pt NMR spectra over 10 000 ppm in breadth, and acquire high-resolution quadrupolar-broadened 17O NMR spectra, among other applications. Furthermore, MOFs and COFs can also act as a matrix for polarizing agents, permitting the homogeneous distribution of radicals and offering promise for further DNP enhancement of framework and guest NMR signals. The current body of published work regarding DNP of MOFs and COFs shows tremendous promise in this experimental avenue and clearly indicates that this will be an active field of research in the years to come.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".