Carbon Dioxide conversion to Carbon nanofibers: Development of the carbonization section
Bibliographic record
Abstract
Carbon nanofibers (CNFs) have received a great deal of attention as an additive to enhance engineering material properties. In recent years, the use of carbon nanofibers for environmental applications and hydrogen storage has increased due to their high surface area. It is the high cost of CNFs that severely limits their wide application in addition to most of the existing production methods relying on fossil fuels.In the prior art, Dr. M. Zarabian and Dr. Pereira, at the University of Calgary succeeded to develop a net-zero emission technology to produce CNFs from greenhouse gases (GHG) as a feedstock. This M.Sc. thesis aims to evolve the carbonization reactor design to be upscaling-ready by introducing a configuration that could provide uniform reactant concentration, effective transfer of heat generated in exothermic reaction and ease of removing solid fibres. To accomplish this goal, first, the bench-scale method had been fully understood and then new concepts to overcome process bottlenecks were investigated. A new configuration of the reactor to fullfill the requirement was proposed. Then computational fluid dynamics (CFD) was employed to study the flow regime inside the new configuration. Based on CFD results, a prototype of a new configuration was built, and different aspects of process and product were investigated. The results showed that the new reactor configuration to produce carbon nanofiber on sheet metal substrate provides faster nucleation, a more uniform conversion rate and a steady rate of product accumulation compared to monolith configuration. In addition, it shows high up-scalability potential. A prototype of an Autodipping machine was built and successfully employed to impregnate catalysts on the disks. A series of standard characterization procedures for different methods were developed and employed to characterize the products and compare them with commercially available products. The evaluations indicated that both disk and monolith configuration CNF products had comparable and even better features than those of commercial ones.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".