GraphIAST: A graphical user interface software for Ideal Adsorption Solution Theory (IAST) calculations

Industrial exhaust gases have a strong environmental impact, including on global warming. Carbon dioxide (CO_2) is a prominent example of such an exhaust gas. Therefore, CO2 capture and storage in industrial processes is becoming increasingly important. Preferably, these emitted gases are separated before their release into the environment. Such applications require selective gas separation to isolate the harmful gases or to allow recycling of industrially relevant gases. Porous materials are promising candidates to achieve gas separation, since their large surface area enables them to adsorb large quantities while their selectivity can be tuned by controlling their chemical composition. Modelling adsorption behavior and calculating corresponding selectivities in multicomponent gas mixtures of such porous materials, which is essential to quantify their gas separation performance, can be achieved through the Ideal Adsorption Solution Theory (IAST), which can be challenging to perform. The current available softwares for IAST calculations demand programming knowledge that not every materials scientist has or has access to, limiting the development of new porous materials for gas separation purposes. In this paper, we present a simple, user-friendly program for IAST loading and selectivity predictions for binary gas mixtures based on the Python module pyIAST. We have developed a graphical user interface resembling commonly known software and made three-dimensional selectivity predictions easily accessible within just a few clicks. The input and output data structure relies on the widely used *.csv format and isotherm data can be fitted with various established models. Therefore, our software provides a platform for IAST calculations for non-programming researchers, which is expected to enable more materials scientists to screen their porous materials for desired gas separation properties.

工业废气对环境具有显著影响，包括加剧全球变暖。二氧化碳（CO₂）是这类废气的典型代表。因此，工业过程中的二氧化碳捕集与封存正愈发重要。理想状态下，这类排放气体应在释放至环境前完成分离。此类应用需借助选择性气体分离技术，以隔离有害气体或回收具备工业应用价值的气体。多孔材料是实现气体分离的极具潜力的候选材料：其较大的比表面积可实现大量气体的吸附，且可通过调控化学组成来调节吸附选择性。若要对这类多孔材料的吸附行为进行建模，并计算多组分混合气体中的对应选择性以量化其气体分离性能，可借助理想吸附溶液理论（Ideal Adsorption Solution Theory，IAST），但该计算过程颇具难度。当前可用的IAST计算软件要求使用者具备编程知识，然而并非每位材料科学家都掌握或能够获取相关编程能力，这一现状限制了用于气体分离的新型多孔材料的研发。本文基于Python模块pyIAST，开发了一款简易易用的程序，可用于二元混合气体的IAST吸附量与选择性预测。我们打造了贴合主流商用软件风格的图形用户界面，仅需数次点击即可便捷获取三维选择性预测结果。该程序的输入与输出数据结构采用广泛使用的*.csv格式，吸附等温线数据可通过多种成熟模型进行拟合。因此，本软件为不具备编程能力的研究人员提供了IAST计算平台，有望助力更多材料科学家筛选其研发的多孔材料，以获得所需的气体分离性能。

搜集汇总

背景与挑战

背景概述

该数据集提供了一个名为GraphIAST的图形用户界面软件，用于简化理想吸附溶液理论（IAST）计算，特别针对非编程研究人员设计，以预测多孔材料在二元气体混合物中的吸附负载和选择性。其特点包括用户友好的界面、基于Python模块pyIAST、支持多种等温线模型拟合，并使用常见的CSV格式进行数据输入输出，旨在加速多孔材料在气体分离应用中的筛选和开发过程。