Influence of alkali metal (Li and Cs) addition to Mo<sub>2</sub>N catalyst for CO hydrogenation to hydrocarbons and oxygenates
Bibliographic record
Abstract
Abstract The aim of this work is to understand the catalytic behaviour of Li and Cs promoted Mo2N for CO hydrogenation to hydrocarbons and oxygenates at the reaction conditions 275–325 °C, 7 MPa, and 30 000 h−1 GHSV. Molybdenum nitrides were synthesized via temperature programmed treatment of ammonium heptamolybdate (AHM) and alkali metal (AM) precursors under continuous gaseous ammonia flow. Unpromoted Mo2N and AM‐Mo2N catalysts were characterized using BET‐pore size, X‐ray diffraction, TPD‐mass of CO, HR‐TEM, and XPS techniques. Nominal loadings of 1, 5, and 10 wt% of Li and Cs were selected for these studies. At a 10 % CO conversion level, the total oxygenate selectivity of 28, 11, and 6.5 % was observed on 5Cs‐Mo2N, 5Li‐Mo2N, and unpromoted Mo2N, respectively. The decreased oxygenate selectivity for unpromoted Mo2N was mainly associated with CO dissociative hydrogenation on Moδ+ sites. On the other hand, improved molecular CO insertion into −CxHy intermediate accelerates the total oxygenate formation on the Cs‐Mo‐N catalyst. However, during nitridation, crystal structure changes were observed in Li‐Mo‐N and the obtained oxygenates selectivity was attributed to the Li2MoO4 phases. At lower AM loadings, the active sites corresponding to oxygenates formation were inadequate, and at higher AM loadings, surface metallic molybdenum decreased the total oxygenate selectivity.
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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".