Atomically Dispersed Mg–N–C Material Supported Highly Crystalline Pt<sub>3</sub>Mg Nanoalloys for Efficient Oxygen Reduction Reaction
Bibliographic record
Abstract
Single-atom or atomically dispersed metal materials have emerged as highly efficient catalysts, but their potential as excellent supports has rarely been reported. In this work, we prepared Mg–N–C materials derived from annealing of a Mg-based metal–organic framework (MOF). By introducing Pt, Mg–N–C not only serves as a platform for anchoring Pt nanoparticles but also facilitates the integration of Mg into the Pt face-centered cubic lattice, resulting in the formation of highly crystalline Pt 3 Mg nanoalloys via the metal–support interfacial interaction. Synchrotron radiation-based X-ray absorption spectroscopy (XAS) enables us to study the interfacial interaction and the surface electronic structure of this intricate system. The formation of Pt 3 Mg nanoalloys induces a downshift of the Pt d-band (gaining d-charge), as revealed by the decrease in the Pt L 3 -edge white-line (WL) area under the curve. This downshift can weaken the binding of oxygen reduction reaction (ORR) intermediates, hence improving the ORR performance.
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".