Biological Neural Network‐Inspired Micro/Nano‐Fibrous Carbon Aerogel for Coupling Fe Atomic Clusters With Fe‐N <sub>4</sub> Single Atoms to Enhance Oxygen Reduction Reaction
Bibliographic record
Abstract
Abstract Nitrogen‐coordinated metal single atoms catalysts, especially with M‐N 4 configuration confined within the carbon matrix, emerge as a frontier of electrocatalytic research for enhancing the sluggish kinetics of oxygen reduction reaction (ORR). Nevertheless, due to the highly planar D 4h symmetry configuration in M‐N 4 , their adsorption behavior toward oxygen intermediates is limited, undesirably elevating the energy barriers associated with ORR. Moreover, the structural engineering of the carbon substrate also poses significant challenges. Herein, inspired by the biological neural network (BNN), a reticular nervous system for high‐speed signal processing and transmitting, a comprehensive structural biomimetic strategy is proposed for tailoring Fe‐N 4 single atoms (Fe SAs) coupled with Fe atomic clusters (Fe ACs) active sites, which are anchored onto chitosan microfibers/nanofibers‐based carbon aerogel (CMNCA‐Fe SA+AC ) with continuous conductive channels and an oriented porous architecture. Theoretical analysis reveals the synergistic effect of Fe SAs and Fe ACs for optimizing their electronic structures and expediting the ORR. The ingenious biomimetic strategy will shed light on the topology engineering and structural optimization of efficient electrocatalysts for advanced electrochemical energy conversion devices.
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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 | 0.000 |
| 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".