A Modeling Framework for the Synthesis of Carbon Nanotubes by RF Plasma Technology
Bibliographic record
Abstract
The novel, cost and energy-efficient synthesis of carbon nanotubes (CNTs) by radio-frequency (RF) induction thermal plasma is a promising process for large scale production of CNTs for industrial and commercial applications. Techniques and conditions for producing larger quantities of CNTs have mainly depended on trial-and-error empirical variations of several operating parameters. Therefore, a detailed kinetic mechanism for CNT production in numerical simulations of an RF plasma system is required for understanding the process and identifying the parameters that mainly influence nanotube production. Such a model could also be used to enhance and optimize the design of synthesis systems.\n In this work, a two-dimensional axisymmetric model was constructed for a RF processing system for the production of CNTs. The plasma field was solved using a computational fluid dynamics (CFD) Eulerian frame of reference. The interactions between the plasma and injected feed particles were considered using momentum, heat and mass transfer source terms in the plasma field governing equations. The trajectory and temperature history of the injected particles into the plasma were computed using a Lagrangian method. The effects of plasma gas composition and the raw material feed rate in the system on the plasma thermo-fluid fields were studied. It was found that when 100% He sheath gas was employed, compared to an Ar-He mixture, the evaporation rate of the injected feedstock particles in the plasma increased due to the higher thermal conductivity of the He gas. Based on the raw material feed rate analysis, a higher loading rate of feedstock reduces down the plasma temperature along the injection zone and increases the average evaporation time of the feedstock in the plasma.\nA chemistry model for the formation of CNTs from feedstock material was implemented into an in-house two-dimensional axisymmetric parallelized CFD code. 36 elementary chemical reactions representing the formation of CNTs were numerically solved in the computational domain parallelized for execution on 64 Central Processing Units (CPUs). It was shown that the reaction rates of key reactions could be adjusted within their uncertainty range to improve the predicted yield of CNTs to within the yield rate reported from the experiments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".