Generation and functionalization of pure graphene flake structures in thermal plasma reactors
Bibliographic record
Abstract
Summary form only given. This project relates to the creation of a specific carbon structure for the replacement of Pt catalyst by a non-noble metal such as Fe functionalized on the carbon support [Proietti, E., et al., 2011]. Pt-nanoparticles are typically used as catalyst materials in a large series of applications, for example in the development of polymer electrolyte membrane fuel cells (PEM-FC) as electrical energy sources for the car industry. One main limiting factor in the demand scale up is the availability and increasing price of Pt. Iron atoms dispersed at the atomic level directly on carbon nanoparticles using nitrogen coordination mimicking the blood structure proved recently to have activities that can rival Pt-based catalyst; the stability of this complex however is lacking and high crystallinity of the support structure proved to strongly improve this parameter. The high temperatures attained for carbon nanoparticle nucleation in thermal plasma reactors enable increased crystallinity, however the control, reproducibility and purity are often lacking in such devices. These are addressed in the present research. Modeling and experimental results related to the design of the flow/energy/ nucleation fields in an IC-Thermal Plasma reactor for the nucleation of carbon nanomaterials, and their specific functionalization is presented. A “properly” designed conical geometry of the reactor enables a fine adjustment of the nucleation zone that minimizes the condensation process and essentially eliminates coagulation of the particles. Very good control over purity and reproducibility are attained through the elimination of recirculation fields. The local high temperature enables the nucleation of pure graphene flakes having between 5-16 atomic planes, and planar structures of typically 50nm×100nm [Pristavita, R., et al., 2011]. The nucleation zone of carbon nanoparticles is modeled, this zone being very stable under varying process conditions and leads to a robust process under parametric fluctuations. Nucleation zones and functionalization zones being separated, nitrogen functionalization on specific pyridinic sites is attained downstream of the nanoflake nucleation. Tests made on this non-noble catalyst in PEM-FC operation highlighted the strong improvement in stability of the catalyst, while increased activities from increased levels of functional sites are still required.
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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".