Moreno Daniel, Nelson Hunter, Cary Grant, Parker Devon, Skaggs Pablo
Missouri State University, Springfield, Missouri 65897, United States.
ACS Omega. 2025 Mar 4;10(10):10139-10151. doi: 10.1021/acsomega.4c08707. eCollection 2025 Mar 18.
This study details the development of a computational adsorption model for predicting thermodynamic adsorption parameters for capacitive deionization (CDI) processes. To do this, multiple starting concentrations and temperatures are needed to predict the best fit value. This is first demonstrated experimentally using an in-house CDI cell with custom heaters, and determining maximum adsorption capabilities for a selected range of conditions. This has been done previously for CDI in the published literature, but here, experimental results are incorporated to provide the best fit to a computational model, which runs transient CDI tests in batch mode over multiple concentrations and temperatures to determine adsorption parameters. This saves the eventual challenge of having to run many different experiments independently to determine such adsorption parameters, the accuracy of which may be questionable subject to different experimental errors. With the model, many parameters can be quickly scanned at once, and adsorption parameters can be determined based on the concentration and temperature values selected, as well as other operating conditions, such as voltage and cell resistance. The computational isotherms are generated using the Gouy-Chapman-Stern (GCS) model, which is common for the lower concentration values used for CDI. The model also considers fixed and mobile chemical charges for enhanced CDI (ECDI) and Faradaic CDI (FaCDI), respectively, which have been examined as alternatives to improve CDI performance. While primarily proof-of-concept, the results obtained here demonstrate the benefits in adsorption capabilities, and energy savings obtained here demonstrate benefits in adsorption capabilities and energy savings for FaCDI, coinciding with higher enthalpies and entropies of adsorption. The model also serves as a benchmark in the future for how the results can be further explored and better fits can be obtained experimentally to confirm stability in the thermodynamic values.
本研究详细介绍了一种计算吸附模型的开发,该模型用于预测电容去离子(CDI)过程的热力学吸附参数。为此,需要多个起始浓度和温度来预测最佳拟合值。首先通过使用带有定制加热器的内部CDI电池进行实验演示,并确定选定条件范围内的最大吸附能力。此前在已发表的文献中已针对CDI进行过此类研究,但在此处,实验结果被纳入以提供与计算模型的最佳拟合,该模型以批处理模式在多个浓度和温度下运行瞬态CDI测试以确定吸附参数。这避免了最终必须独立进行许多不同实验来确定此类吸附参数的挑战,而这些参数的准确性可能因不同的实验误差而受到质疑。借助该模型,可以一次快速扫描许多参数,并可根据所选的浓度和温度值以及其他操作条件(如电压和电池电阻)来确定吸附参数。计算等温线是使用Gouy-Chapman-Stern(GCS)模型生成的,该模型常用于CDI中使用的较低浓度值。该模型还分别考虑了增强型CDI(ECDI)和法拉第CDI(FaCDI)的固定和移动化学电荷,它们已被视为改善CDI性能的替代方案进行研究。虽然主要是概念验证,但此处获得的结果证明了吸附能力方面的优势,而此处获得的节能效果证明了FaCDI在吸附能力和节能方面的优势,这与更高的吸附焓和熵相吻合。该模型还可作为未来的一个基准,用于进一步探索结果以及通过实验获得更好的拟合以确认热力学值的稳定性。