Impact of the Oxygen Defects and the Hydrogen Concentration on the Surface of Tetragonal and Monoclinic ZrO<sub>2</sub> on the Reduction Rates of Stearic Acid on Ni/ZrO<sub>2</sub>
Bibliographic record
Abstract
The role of the specific physicochemical properties of ZrO2 phases on Ni/ZrO2 has been explored with respect to the reduction of stearic acid. Conversion on pure m-ZrO2 is 1.3 times more active than on t-ZrO2 , whereas Ni/m-ZrO2 is three times more active than Ni/t-ZrO2 . Although the hydrodeoxygenation of stearic acid can be catalyzed solely by Ni, the synergistic interaction between Ni and the ZrO2 support causes the variations in the reaction rates. Adsorption of the carboxylic acid group on an oxygen vacancy of ZrO2 and the abstraction of the α-hydrogen atom with the elimination of the oxygen atom to produce a ketene is the key to enhance the overall rate. The hydrogenated intermediate 1-octadecanol is in turn decarbonylated to heptadecane with identical rates on all catalysts. Decarbonylation of 1-octadecanol is concluded to be limited by the competitive adsorption of reactants and intermediate. The substantially higher adsorption of propionic acid demonstrated by IR spectroscopy and the higher reactivity to O2 exchange reactions with the more active catalyst indicate that the higher concentration of active oxygen defects on m-ZrO2 compared to t-ZrO2 causes the higher activity of Ni/m-ZrO2 .
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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".