Oxidization-induced structural optimization of Ni3Fe-N-C derived from 3D covalent organic framework for high-efficiency and durable oxygen evolution reaction
Bibliographic record
Abstract
NiFe composites have been regarded as promising candidates to replace commercial noble-based electrocatalysts for the oxygen evolution reaction (OER). However, their practical applications still suffer from poor conductivity, limited activity, and durability. To address these issues, herein, by utilizing three-dimensional covalent organic framework (3D-COF) with porous confined structures and abundant coordinate N sites as the precursor, the partially oxidized Ni 3 Fe nanoalloys wrapped by N-doped carbon (N-C) layers are constructed via simple pyrolysis and subsequent oxidization. Benefiting from the 3D curved hierarchical structure, high-conductivity of Ni 3 Fe and N-C layers, and well-distributed active sites, the as-synthesized O-Ni 3 Fe-N-C catalyst demonstrates excellent activity and durability for catalyzing OER. Experimental and theoretical analyses disclose that both high-temperature oxidization and the OER process greatly promote the formation and exposure of the Ni(Fe)OOH active species as well as lower charge transfer resistance, inducing its optimized OER activity. The robust graphitized N-C layers with superior conductivity and their couplings with oxidized Ni 3 Fe nanoalloys are beneficial for stabilizing catalytic centers, thereby imparting O-Ni 3 Fe-N-C with such outstanding stability. This work not only provides a rational guidance for enriching and stabilizing high-activity catalytic sites towards OER but also offers more insights into the structural evolution of NiFe-based OER catalysts.
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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".