Probing the Interfacial Interaction in Layered-Carbon-Stabilized Iron Oxide Nanostructures: A Soft X-ray Spectroscopic Study
Bibliographic record
Abstract
We have stabilized the iron oxide nanoparticles (NPs) of various sizes on layered carbon materials (Fe-oxide/C) that show excellent catalytic performance. From the characterization of X-ray absorption spectroscopy (XAS), X-ray emission spectroscopy (XES), scanning transmission X-ray microscopy (STXM) and X-ray magnetic circular dichroism spectroscopy (XMCD), a strong interfacial interaction in the Fe-oxide/C hybrids has been observed between the small iron oxide NPs and layered carbon in contrast to the weak interaction in the large iron oxide NPs. The interfacial interaction between the NPs and layered carbon is found to link with the improved catalytic performance. In addition, the Fe L-edge XMCD spectra show that the large iron oxide NPs are mainly γ-Fe2O3 with a strong ferromagnetic property, whereas the small iron oxide NPs with strong interfacial interaction are mainly α-Fe2O3 or amorphous Fe2O3 with a nonmagnetic property. The results strongly suggest that the interfacial interaction plays a key role for the catalytic performance, and the experimental findings may provide guidance toward rational design of high-performance catalysts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".