Enhancing the ORR durability of single atomic Fe-N4 active sites with implanted SiO2 nanoparticles as radical and H2O2 inhibitors
Bibliographic record
Abstract
Highly efficient and durable single-atom catalysts (SACs) hold great promise for improving oxygen reduction reaction (ORR) in metal-air batteries and fuel cells. However, their long-term stability is challenged by the byproducts such as H2O2 and undesirable radicals. Herein, we report a Fe-N4 active center-based SAC decorated with SiO2 nanoparticles (NPs) as a radical scavenger, which was prepared using coffee grounds and industrial spent acid residue. The presence of SiO2 NPs effectively suppresses the electrochemical H2O2 production, significantly improving durability with only a 5 mV half-wave potential loss after 30,000 voltage cycles in alkaline media. Electrochemical evaluations, in-situ characterizations, and density functional theory calculations reveal that the Fe-O-Si binding at the SiO2–Fe-N4 interface strengthens the binding of OOH* species, facilitating the 4-electron selectivity in ORR while inhibiting the formation of H2O2 and reactive oxygen species. Additionally, the SiO2 NPs prevent the aggregation of Fe single atomic sites, thereby stabilizing the SAC active sites. Therefore, the incorporation of SiO2 NP into Fe-based SAC offers a straightforward and effective strategy for enhancing ORR performance. The authors report a Fe–N4 single-atom catalyst decorated with SiO2 nanoparticles acting as radical scavengers, which reduce harmful byproducts and stabilize active sites, improving durability and activity for oxygen reduction.
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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".