Optimizing the synthesis of Ag/<scp>γ‐Al<sub>2</sub>O<sub>3</sub></scp> for selective reduction of <scp>NOx</scp> with <scp>C<sub>3</sub>H<sub>6</sub></scp>: Experiments and modelling
Bibliographic record
Abstract
Abstract Ag/γ‐Al2O3 is an effective catalyst for the selective reduction of NOx (SCR) using propylene as a reducing agent. The catalyst performance is greatly influenced by the synthesis procedure. Various methods for synthesis of Ag/γ‐Al2O3 are analyzed, and their performance is examined via packed bed reactor experiments in this work. An optimal one‐pot synthesis method, single‐step sol–gel (SSG) synthesis, is explored systematically. The SSG‐synthesized catalyst shows better performance than those prepared via wet impregnation. The influence of synthesis conditions, specifically pH, on the textural and morphological properties of the SSG‐synthesized Ag/γ‐Al2O3, and therefore the activity for hydrocarbon‐based SCR in a packed‐bed reactor, are analyzed using experiments and simulations. The optimized catalyst demonstrates excellent performance (90% NOx conversion) for NOx reduction under nominal operating conditions with a wide activity temperature window (300–600°C). The catalyst shows good time‐on‐stream performance and is effective at higher inlet oxygen concentrations and space velocities. A global kinetic model, which uses synthesis‐pH‐dependent parameters, is proposed, and its ability to predict the activities of these catalysts is validated.
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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".