Tailoring Carbon Nanotube Microsphere Architectures with Controlled Porosity
Bibliographic record
Abstract
Abstract Nanomaterials are at the core of fuel cell electrodes, providing high‐area catalytic, proton, and electron conducting surfaces, traditionally on carbon black supports. Other carbons, e.g., carbon nanotubes (CNTs) and graphene are less prone to oxidation; however, their handling is not trivial due to health risks associated with their size. Assembling them into microscale structures without jeopardizing their performance is ideal, but there are mass transfer limitations as thickness increases. In this work, a soluble acicular calcium carbonate (aragonite) is used as a porogen to create connected porosity in microspheres. Increasing macroporosity has a considerable positive impact on the mass transfer process. The experimental manipulation of porosity of the microspheres is combined with pore network modeling to better understand how pore distribution throughout the whole microsphere can optimize platinum utilization decorated onto the CNTs. Oxygen reduction reaction (ORR) activity is compared with the prepared composite materials and a commercial Pt/C catalyst for 4 weeks. The composite materials exhibit a highly interconnected network resulting in a 3.4 times higher ORR activity (at 0.9 V vs reversible hydrogen electrode) than that of the nanoporous spheres with no macroporosity.
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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 | 0.000 |
| 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 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".