Alkyl-imidazolium templating synthesis to extra-large-pore aluminogermanosilicate zeolite IRR with enhanced Al substitution and excellent catalytic performance in bulky molecule acetalization

Li, Haojie; Hou, Xiaozhe; Zhang, Ling; Wei, Ying; Liang, Yan; Qiao, Junjun; Su, Haohao; Gao, Xibing; Jia, Jingbo; Zhang, Runduo

doi:10.1007/s10934-023-01422-6

Cited by 1 publication

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“…In Figure , a box plot presenting the distribution of all TSN scores for each structure composed of the IRR topology identifies IRR_2 as a particularly promising structure out of this group. The IRR topology has been synthesized as either a germanosilicate or aluminogermanosilicate in the past, − and the results from this work indicate that this topology could be worth exploring as a potential candidate for C2/C3 TSA separation in its aluminosilicate form.…”

Section: Resultsmentioning

confidence: 98%

Machine Learning and IAST-Aided High-Throughput Screening of Cationic and Silica Zeolites for Alkane Capture, Storage, and Separations

Daou,

Fang,

Boulfelfel

et al. 2024

J. Phys. Chem. C

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We present an approach for quantitatively predicting the temperature-dependent single-component adsorption behavior of linear alkanes in silica and Na-exchanged cationic zeolites using machine learning (ML) models trained from extensive molecular simulations based on force fields with coupled cluster accuracy. A high-performing classification model was developed to distinguish between instances with negligible and non-negligible adsorption. Subsequently, two ML models were trained to predict the single-component adsorption loading and the heat of adsorption at any pressure at 300 K for any zeolite topology and silicon-to-aluminum ratio. The ML models were trained on International Zeolite Association (IZA) zeolites, and their transferability to hypothetical zeolites was successfully validated. We then expand the power of these predictions to adsorbed mixtures at arbitrary temperatures by integrating them with the Clausius−Clapeyron equation and ideal adsorbed solution theory (IAST). This approach was validated and then applied to a temperature swing adsorption separation process to demonstrate its practical utility. We demonstrate how predictions from this ML-enabled approach can allow the selection of high-performing materials that are then validated using detailed molecular simulations based on quantitatively accurate force fields.

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