Mechanistic studies on the conversion of NO gas on urea-iron and copper metal organic frameworks at low temperature conditions: in situ infrared spectroscopy and Monte Carlo investigations
Bibliographic record
Abstract
Nitrogen oxide (NOx) emissions from high-temperature combustion processes under fuel-lean conditions continue to be a challenge for the energy industry. Selective catalytic reduction (SCR) is possible using metal oxides and zeolites. There is still a need to identify catalytic materials that are efficient in reducing NOx to environmentally benign nitrogen gas at temperatures lower than 200 °C. Metal-organic frameworks (MOFs) have emerged as a class of highly porous materials with unique physical and chemical properties. This study is motivated by the lack of systematic investigations on SCR using MOFs under industrially relevant conditions. Here, we investigate the extent of NO conversion with two commercially available MOFs, Basolite F300 (Fe-BTC) and HKUST-1 (Cu-BTC), mixed with solid urea as a source for the reductant, ammonia gas. For comparison, experiments were also conducted using cobalt ferrite (CoFe2O4) as a non-porous counterpart to relate its reactivity to those obtained from MOFs. Fourier-transform infrared spectroscopy (FTIR) was utilized to identify the gas and surface species in the temperature range of 115–180 °C. Computational analysis was performed using Monte Carlo simulations to quantify the adsorption energies of different surface species. The results show that the rate of ammonia production from the in situ solid urea decomposition was higher using CoFe2O4 than Fe-BTC and Cu-BTC and that there was very limited conversion of NO on the mixed solid urea-MOF systems due to site blocking. The main conclusions from this study are that MOFs have limited ability to convert NO under low-temperature conditions and that surface regeneration requires additional experimental steps.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".