Aluminum‐Rich Molecular Sieve Interface Regulating Electron‐Enriched Mn‐O‐Ce Active Sites for Environmental Catalysis
Bibliographic record
Abstract
Abstract The improvement of low‐temperature catalytic activity still remains a paramount scientific challenge in environmental catalysis. To address this issue, an aluminum‐rich molecular sieve interface regulating electron‐enriched Mn‐O‐Ce active sites for highly efficient environmental catalysis has been demonstrated. Specifically, the surface aluminum‐rich hollow ZSM‐5 zeolites are constructed through dissolution‐recrystallization and coupled with MnCeO x composites owning strong redox properties to amplify contact between acidic and active sites that manipulate effective environmental catalytic reactions. Taking the selective catalytic reduction of nitrogen oxide (NO x ) as a probing reaction, the engineered Al‐rich interface significantly facilitates the electron transfer from ZSM‐5 zeolite to MnCeO x composite, creating electron‐enriched Mn‐O‐Ce active sites that artfully establish adjacent centers for reactant molecules adsorption and activation: Mn‐end of Mn‐O‐Ce sites for NO x coordination and Ce‐end of Mn‐O‐Ce sites for partial ammonia (NH 3 ) adsorption to achieve superior catalytic activity and selectivity below 150 °C. Concurrently, the modulated zeolite‐metal oxide interface with electron‐enriched Mn‐O‐Ce active sites and sufficient Brønsted acid sites also demonstrates exceptional efficiency in synergistic removal of nitrogen oxide with representative volatile organic compounds. Beyond superior multifunctional catalytic performance, this work pioneers interfacial electron engineering as a universal strategy to design advanced functional materials for efficient environmental catalysis.
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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".