Metadata-Version: 2.4
Name: CarbonX
Version: 0.2.1
Summary: Process Design Tool for Gas-Phase Synthesis of Metallic Nanoparticles, Carbon Nanotubes, and Graphene Layers
Home-page: https://github.com/Hsnrahbar/CarbonX_Package
Author: Hossein Rahbar
Author-email: rahbar.hosein@example.com
Keywords: Carbon Nanotube,Metallic Nanoparticles,Machine Learning
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: License :: Other/Proprietary License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Cython
Classifier: Operating System :: Microsoft :: Windows
Requires-Python: >=3.8,<3.12
Description-Content-Type: text/markdown
License-File: LICENSE.txt
Requires-Dist: numpy<2.0.0,>=1.24.0
Requires-Dist: scipy<1.14.0,>=1.10.0
Requires-Dist: matplotlib>=3.5.0
Requires-Dist: pandas<2.1.0,>=1.5.0
Requires-Dist: cantera>=2.6.0
Requires-Dist: pyyaml>=6.0
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: keywords
Dynamic: license-file
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

CarbonX

CarbonX is an object-oriented Python package for simulating floating-catalyst chemical vapor deposition (FCCVD) reactors and predicting the gas-phase synthesis of metallic nanoparticles, carbon nanotubes, and graphene.

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OVERVIEW

Carbon nanotubes (CNTs) exhibit exceptional electrical, optical, and mechanical properties, making them key materials for applications in energy storage, sensing, and advanced composites.

CarbonX provides a modular and extensible simulation framework to model CNT growth by capturing the interactions between chemical kinetics, catalyst evolution, and particle dynamics.

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KEY FEATURES

- Object-oriented design
- Fully coupled multiphysics simulation
- Modular and extensible architecture
- Supports CNTs, graphene, amorphous carbon, and metal nanoparticles (e.g., Fe, Ni, Co)
- Time- and space-resolved simulations
- Integrated machine learning module for process optimization

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ARCHITECTURE

Chemical Kinetics
Models gas-phase reactions.

Surface Kinetics
Handles catalyst activation, deactivation, and hydrogenation.

Particle Dynamics
Simulates nanoparticle inception, growth, coagulation, and sintering.

CNT Dynamics
Predicts CNT length, diameter, and wall number.

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MACHINE LEARNING MODULE

Analyzes parametric maps to identify optimal synthesis conditions such as temperature, pressure, and catalyst concentration.

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EXTENSIBILITY

Users can integrate custom:
- Gas-phase mechanisms
- Surface kinetics models
- Particle or CNT growth models

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CONTACT

Hossein Rahbar
Email: rahbar.hosein@gmail.com