Characterizing Substrate–Surface Interactions on Alumina-Supported Metal Catalysts by Dynamic Nuclear Polarization-Enhanced Double-Resonance NMR Spectroscopy
Bibliographic record
Abstract
The characterization of nanometer-scale interactions between carbon-containing substrates and alumina surfaces is of paramount importance to industrial and academic catalysis applications, but it is also very challenging. Here, we demonstrate that dynamic nuclear polarization surface-enhanced NMR spectroscopy (DNP SENS) allows the unambiguous description of the coordination geometries and conformations of the substrates at the alumina surface through high-resolution measurements of 13 C– 27 Al distances. We apply this new technique to elucidate the molecular-level geometry of 13 C-enriched methionine and natural abundance poly(vinyl alcohol) adsorbed on γ-Al 2 O 3 -supported Pd catalysts, and we support these results with element-specific X-ray absorption near-edge measurements. This work clearly demonstrates a surprising bimodal coordination of methionine at the Pd–Al 2 O 3 interface.
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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 | 0.000 | 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) | 0.000 | 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".