Synthesis and Characterization of Carbon Nanostructures as Catalyst Support for PEMFCs
Bibliographic record
Abstract
A detailed procedure for synthesis, characterization, and possibility of carbon nanostructures (CNS) as support for catalysts in polymer electrolyte membrane fuel cells (PEMFCs) is presented. The fabrication process is two-staged ballmilling of carbon graphite in the presence of hydrogen and transition metals (Fe, Co) followed by heating of the milled carbon initially in an argon atmosphere. The milling induces amorphous forms of carbon and metal, as well as C-H bonds. During the second stage, the production of methane by catalytic reaction of the bonded carbon and hydrogen is first observed, followed by the formation of metallic nanocrystals, and, finally, the formation of carbon structures on the metallic nanocrystals at a temperature of 700 ° C . Subsequently, metals and carbon nanoparticles are removed from the as-prepared sample. The purified samples are platinized after surface treatment by either air or chemical oxidation. Material characterization results obtained by X-ray diffraction, transmission electron microscopy, thermogravimetric analysis, X-ray photoelectron spectrocopy, and atomic adsorption spectroscopy are presented. In addition, we also report their measured electrical conductivity, specific surface, and porosity. The real electrochemical active surface area was evaluated by cyclic voltammetry on a thin porous coated electrode.
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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 | 0.001 |
| 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".