High Oxygen Storage Capacity of Ultrasmall Mn-Doped CeO<sub>2</sub> Nanoparticles via Enhanced Local Distortion and Mn(II) Lattice Substitution
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Oxygen storage materials are crucial for energy conversion and storage processes. Their high-temperature (HT ≥ 300 °C) and low-temperature (LT < 300 °C) oxygen storage capacities (OSC) directly influence reaction feasibility, pathway, rate, and selectivity. Precise control over the composition and local structure of metal oxide nanoparticles is essential for developing high-performance oxygen carriers. However, achieving such microstructure control, along with highly reproducible synthesis, has remained elusive, and the relationship between composition/local structure/chemical state and HT/LT OSC is largely unexplored. In this study, Mn-doped CeO 2 nanoparticles (Mn-CeO 2 NPs) were synthesized using a continuous-flow hydrothermal system. Superheated water and a benzene solution containing Ce/Mn precursors were rapidly mixed in a micromixer to ensure quick heating and uniform reaction conditions. The flow synthesis strategy facilitated fast quenching of the growth process (cooling to room temperature), allowing for the successful synthesis of Mn-CeO 2 NPs with an average crystallite size of less than 5 nm. The composition (Mn-ICP: 0–33.6 mol %), local structure, and oxidation state of Mn species in CeO 2 NPs were well controlled by adjusting the synthesis parameters including temperature, flow rate, and Mn loading concentration. Compared to the batch method, the continuous-flow supercritical approach yielded Mn-CeO 2 NPs with LT OSC at least four times higher. By integrating all sample information, the intrinsic activation mechanism of the Mn dopant for LT and HT OSC was revealed. HT OSC is linearly correlated with Mn concentration and is enhanced by Mn-induced local structure distortion, as evidenced by a reductive chemical shift in Ce L 3 -edge XAS. For LT OSC, the Mn lattice substitution state plays a crucial role, and among Mn species, a higher proportion of Mn 2+ in the CeO 2 lattice contributes to greater LT OSC at similar Mn contents. These findings offer effective strategies for tailoring the low-temperature and high-temperature redox activities of oxygen carriers, reducing reliance on time-consuming empirical methods.
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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".