PyICLab: An integrated Python-based toolkit for in-silico simulations of ion chromatography.

IF 5.6 1区化学 Q1 CHEMISTRY, ANALYTICAL Talanta Pub Date : 2025-01-01 Epub Date: 2024-10-15 DOI:10.1016/j.talanta.2024.127054

Kai Zhang, Yule Qian, Chaoyan Lou, Mingli Ye, Yan Zhu

引用次数: 0

Abstract

PyICLab is an open-source Python-based package featuring an object-oriented programming (OOP) interface, providing tools for realistic and customized numerical simulations of ion chromatography (IC). In this paper, we showcase PyICLab's use in simulating diverse separation scenarios, including isocratic carbonate elution, gradient hydroxide elution, high-concentration and large-volume injections. The accuracy of the embedded models was validated by demonstrating strong correlations between predicted and experimental results. Additionally, PyICLab's capability to handle complex IC configurations was demonstrated through a simulation of a column-switching system for seawater analysis. PyICLab offers valuable resources for chromatographic optimization, method development, and educational purposes. It is available on PyPI at pypi.org/project/pyIClab. Interested readers can install PyICLab using the pip command in a Python 3.11 or higher environment.

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PyICLab：基于 Python 的集成工具包，用于离子色谱法的内部模拟。

PyICLab 是一个基于 Python 的开源软件包，具有面向对象编程（OOP）界面，为离子色谱（IC）的逼真和定制化数值模拟提供了工具。在本文中，我们展示了 PyICLab 在模拟各种分离场景中的应用，包括等度碳酸盐洗脱、梯度氢氧化物洗脱、高浓度和大体积进样。通过证明预测结果与实验结果之间的强相关性，验证了嵌入模型的准确性。此外，PyICLab 处理复杂集成电路配置的能力还通过模拟用于海水分析的色谱柱切换系统得到了验证。PyICLab 为色谱优化、方法开发和教育目的提供了宝贵的资源。它可从 PyPI 上获取，网址是 pypi.org/project/pyIClab。感兴趣的读者可以在 Python 3.11 或更高版本的环境中使用 pip 命令安装 PyICLab。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Talanta 化学-分析化学

CiteScore

12.30

自引率

4.90%

发文量

861

审稿时长

29 days

期刊介绍： Talanta provides a forum for the publication of original research papers, short communications, and critical reviews in all branches of pure and applied analytical chemistry. Papers are evaluated based on established guidelines, including the fundamental nature of the study, scientific novelty, substantial improvement or advantage over existing technology or methods, and demonstrated analytical applicability. Original research papers on fundamental studies, and on novel sensor and instrumentation developments, are encouraged. Novel or improved applications in areas such as clinical and biological chemistry, environmental analysis, geochemistry, materials science and engineering, and analytical platforms for omics development are welcome. Analytical performance of methods should be determined, including interference and matrix effects, and methods should be validated by comparison with a standard method, or analysis of a certified reference material. Simple spiking recoveries may not be sufficient. The developed method should especially comprise information on selectivity, sensitivity, detection limits, accuracy, and reliability. However, applying official validation or robustness studies to a routine method or technique does not necessarily constitute novelty. Proper statistical treatment of the data should be provided. Relevant literature should be cited, including related publications by the authors, and authors should discuss how their proposed methodology compares with previously reported methods.