Magnesium oxide modified with various iodine‐containing compounds––Surface studies
Bibliographic record
Abstract
The activity of iodine‐modified MgO in transfer hydrogenation of acrolein with alcohols can be significantly higher than that of unmodified MgO. Data from literature and our previous studies indicate that the initial source of a halide influences the surface properties and hence the activity of the obtained catalytic system. The aim of this paper is to present a systematic study on the influence of the initial source of a halide on the activity and surface properties of MgO‐based catalysts. For the sake of comparison, results for an MgO sample subjected to the same procedure as the iodine‐modified catalysts (MgO‐CH 3 OH) are included in the manuscript. The following order of activity was observed: MgO‐I 2 > MgO‐MgI 2 > MgO‐HI > > MgO‐KI > MgO = MgO‐CH 3 OH. The second part of the study consisted of characterization studies aimed at determining the strengths of Brønsted basic and acidic sites, total concentrations of acidic and basic sites, topography of the surfaces, elemental composition, differences in the chemical environment of the surface ions, as well as relative amounts of different bands in an attenuated total reflectance–Fourier transform infrared spectrum, which correspond to the bonds present on the surface of the samples. Some parameters were independent of the iodine precursor used (eg, the breadth of the Mg 2p signal), whereas others strongly varied from one sample to another (eg, the range of strengths of the Brønsted acidic and basic sites). The ratio of 2 carbonate bands of the attenuated total reflectance–Fourier transform infrared spectra of the modified catalysts shows a trend which corresponds to the order of activity.
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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) | 0.001 | 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".