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.

工业废气对环境影响显著，其中全球变暖效应尤为突出。二氧化碳（CO₂）便是这类废气的典型代表。因此，工业过程中的二氧化碳捕获与封存正变得愈发重要。理想情况下，这类排放气体应在释放至环境前完成分离。此类应用需要实现选择性气体分离，以隔离有害气体，或是回收工业用途相关气体。多孔材料是实现气体分离的理想候选材料：其高比表面积可实现大量气体吸附，且通过调控化学组成即可调节吸附选择性。要量化多孔材料的气体分离性能，需对其在多组分混合气体中的吸附行为进行建模并计算对应吸附选择性，而这可通过理想吸附溶液理论（Ideal Adsorbed Solution Theory, IAST）实现，但该理论的应用颇具挑战性。当前可用的IAST计算软件均要求使用者具备编程知识，但并非所有材料科学家都掌握或能够获取这类技能，这限制了用于气体分离的新型多孔材料的研发进展。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。我们开发了类似主流商用软件的图形用户界面，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，且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具备目标气体分离性能的多孔材料。