Promotion of Ilmenite Blending on the Antisulfur Performance of the Lithium–Silicon-Powder-Derived Low-Temperature NH<sub>3</sub>–SCR Catalyst
Bibliographic record
Abstract
Lithium–silicon-powder waste, combined with ferromanganese ore (MnFe/Z), shows great promise as a cost-effective low-temperature NH 3 –SCR catalyst for NO x emission control. However, the MnFe/Z catalyst’s low sulfur tolerance limits its applications. A natural ilmenite (NI)-doped catalyst (NI x –MnFe/Z) was carefully studied and optimized to address this deficiency. The results revealed that natural ilmenite doping enhanced MnFe/Z’s antisulfur performance without negatively affecting catalytic activity in the low-temperature range (125–200 °C). The optimized NI 3 –MnFe/Z had the best catalytic activity, maintaining 96.5% NO conversion after 6 h under 50 ppm of SO 2 at 175 °C. The introduced NI suppressed the electron transfer from Mn 4+ and Fe 3+ to SO 2 and reduced sulfate formation, effectively protecting the active sites from SO 2 poisoning. NI addition protected the redox properties of Mn and Fe and the acidity of the catalyst; the NI 3 –MnFe/Z catalyst broke the SO 2 poisoning obstacles in low-temperature NH 3 –SCR toward future application for efficient NO elimination from industrial flue gas.
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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".