KinOpt

The aim of the KinOpt project is to use kinetics data to perform isoconversional analysis and model optimization.

Table of Contents

• Getting Started
  • Prerequisites
  • Installation
• Usage
• Features
  • Isoconversional analysis
  • Kinetic models
  • Model optimization
• Contributing
• License
• Acknowledgments

Getting Started

Prerequisites

The first step is to obtain experimental kinetic data (e.g. from FTIR, DSC or rheological experiments). These data should be stored as text or csv files containing four columns arranged in the following order:

1. Time (in seconds)
2. Temperature (in Kelvin)
3. Reaction speed (in s-1)
4. Extent of reaction (in %)

This project uses Python language with the following libraries:

• numpy
• scipy
• matplotlib
• pyqt5
• tqdm

We acknowledge and appreciate their contributions to the Python community.

Most of these modules are pre-installed, else you can install them from PyPI with pip:

python -m pip install missing_module_name

or with conda:

conda install missing_module_name

Installation

To install this module, simply download the project in a zip file and extract it.

Usage

To use this project, go to kinopt/src and run main.py with python. The following graphical interface should appear:

Features

Isoconversional analysis

Kinetic models

Model optimization

Contributing

Explanation on how others can contribute to thr project.

1. Fork the repository
2. Create a new branch
3. Make changes and commit them
4. Push to the branch
5. Open a pull request

License

Copyright (c) 2024, Alan Tabore. All rights reserved.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.

2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

3. Neither the name of Alan Tabore nor the names of any contributors may be used to endorse or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

Acknowledgments

We would like to express our gratitude to the following individuals, organizations, and projects for their contributions and support to this project:

• NumPy and SciPy teams for their invaluable libraries that power scientific computing in Python.
• Matplotlib developers for providing an extensive plotting library for Python.
• PyQt5 developers for their powerful cross-platform GUI toolkit.
• tqdm developers for their handy progress bar utility.
• Python Software Foundation for maintaining the Python programming language and its rich standard library.

We are grateful to the open-source community for their continuous contributions, bug reports, and feedback, which help improve this project over time.