Mesoporous Fe‐N<sub>x</sub>‐C Sub‐Microspheres for Highly Efficient Electrocatalytic Oxygen Reduction Reaction
Bibliographic record
Abstract
Abstract We design and obtain mesoporous Fe‐N x ‐C sub‐microspheres (Meso Fe‐N x ‐C‐SS) using the amino acid‐metal complex as Fe/N/C precursor and mesoporous silica sub‐microspheres (MSS) as hard template by a facile template‐assistance strategy. The Histidine‐Fe complex is impregnated into mesopores of MSS, and then Meso Fe‐N x ‐C‐SS are obtained by high‐temperature carbonization process and removal of MSS template. The as‐fabricated Meso Fe‐N x ‐C‐SS exhibits the spherical structure with rough surface with the diameter size of 200–500 nm. And Meso Fe‐N x ‐C‐SS also possesses a high specific surface area of 784 m 2 g −1 , a large pore volume of 1.65 cm 3 g −1 , and a large pore size in the range of 10–22 nm. Benefiting from atomically dispersed N‐coordinated Fe and unique three‐dimensionally (3D) interconnected mesoporous carbon architecture, Meso Fe‐N x ‐C‐SS shows excellent electrocatalytic performance toward oxygen reduction reaction (ORR) with positive half‐wave potential (E 1/2 0.86 V), high stability (93.94 % after 30,000 s), low Tafel slop (43 mV dec −1 ) and good methanol tolerance, exceeding commercial Pt/C catalyst in alkaline media. As the cathode catalyst within the primary Zn‐air batteries, Meso Fe‐N x ‐C‐SS also reveals an outstanding catalytic activity (the maximum power density of 120 mW cm −2 ) and superior discharge stability (at current density of 10 mA cm −2 after 6 cycles, at different current densities of 5, 10, 20 mA cm −2 ), promoting its practical application in Zn‐air batteries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".