Intensified Catalytic Decomposition of Acetone at Room Temperature Using a Ag-Modified CeO<sub>2</sub>–Al<sub>2</sub>O<sub>3</sub> Binary Metal Oxide Support: Enhancing Synergies, Role of Relative Humidity, and In Situ Mechanistic Interpretation
Bibliographic record
Abstract
This study probes the effectiveness of using a Ag/CeO 2 –Al 2 O 3 mixed metal oxide support compared to Ag-modified single supports (Ag/CeO 2 and Ag/Al 2 O 3 ) on acetone removal under VUV irradiation at room temperature. It is shown that under VUV light, the type of support can affect acetone oxidation at the microscopic and macroscopic levels. At the microscopic level, the findings from X-ray photoemission spectroscopy (XPS) and X-ray absorption spectroscopy (XAS) analyses showed that the nature of the support can influence the oxidation state of silver. At the macroscopic level, it was demonstrated that the support can control the dominance of the oxidation mechanism. While Ag/Al 2 O 3, compared to Ag/CeO 2, can boost acetone and ozone conversion, the selectivity of Ag/Al 2 O 3 (88%) was lower than that of Ag/CeO 2 (96%). However, not only can Ag/CeO 2 –Al 2 O 3 with an optimized 1:1 ratio of CeO 2 /Al 2 O 3 oxidize 96 and 98% of the inlet acetone and ozone, respectively, but also the reaction selectivity was above 97%. Moreover, the influence of relative humidity (RH) on Ag/CeO 2 –Al 2 O 3 activity under VUV light was investigated, and it proved the dual character of RH. Although RH improved the VUV photolysis performance in the gaseous state, it poisoned the gas–catalyst interface, leading to an inhibition role in the catalytic reactions. The high and sustainable performance of the Ag/CeO 2 –Al 2 O 3 catalyst at room temperature, achieved through engineering of the mixed metal oxide support and maintained even under humid conditions, offers a promising solution for indoor air quality control in diverse settings. These include residential, commercial, and industrial spaces and potential applications in reducing volatile organic compounds (VOCs) from automotive emissions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".