Sterical Self-Consistency of Carbonaceous Nanopolyhedra Triggered by Introduced CNTs to Optimize ORR Performance
Bibliographic record
Abstract
The electrocatalyst of oxygen reduction reactions is one of the basic components of a fuel cell. In addition to costly Pt/C benchmark catalysts, cost-effective carbon-based catalysts have received the most attention. Enormous efforts have been dedicated to trade off the catalyst performance against the economic benefit. Optimizing composition and/or structure is a universal strategy for improving performance, but it is typically limited by tedious synthesis steps. Herein, we have found that directly introducing CNT into MOF-derived carbonaceous nanopolyhedra, i.e., introduced carbon nanotubes (CNTs) penetrated porous nitrogen-doped carbon polyhedra (NCP) dotted with cobalt nanoparticles (denoted as CNTs-Co@NCP), can optimize the catalytic activity, stability, and methanol tolerance. The hierarchical architecture combines the 0D/1D/3D Co/CNT/NCP interfaces and 1D/3D CNT/NCP junctions with the frameworks with a greatly exposed active surface, strengthened mass transport kinetics, stereoscopic electrical conductivity networks and structural robustness. The sterical self-consistency of MOF-self-assembly triggered by introduced CNTs demonstrates tactful ORR electrocatalytic activity regulation. Eventually, the CNTs-Co@NCP showed a half-wave potential (E1/2) of 0.86 V and a diffusion-limited current density (JL) of 5.94 mA/cm2 in alkaline electrolyte. The CNTs-Co@NCP was integrated into the cathode of a direct methanol fuel cell (DMFC) with an anion-exchange membrane, and an open-circuit voltage (OCV) of 0.93 V and a high power density of 46.6 mW cm−2 were achieved. This work successfully developed a catalyst with competitive ORR performance through plain parameter fine-tuning without complex material design.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".