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 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) | 0.000 | 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 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".