Predicting hydrogen storage in MOFs via machine learning.
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ABSTRACT:
SUBMITTER: Ahmed A
PROVIDER: S-EPMC8276024 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Extensive research has been devoted to developing metal nanoparticle (NP) doped porous materials with large hydrogen storage capacity and high hydrogen release pressure at ambient temperature. The ultra-sound assisted double-solvent approach (DSA) was applied for sample synthesis. In this study, tiny Pd NPs are confined into the pore space of HKUST-1, affording Pd@HKUST-1-DS with minimizing the aggregation of Pd NPs and subsequently the formation of Pd NPs on the external surface of HKUST-1. The experimental data reveal that the obtained Pd NP doped Pd@HKUST-1-DS possessed an outstanding hydrogen storage capacity of 3.68 wt% (and 1.63 wt%) at 77 K and 0.2 MPa H2 (and 298 K and 18 MPa H2), in comparison with pristine HKUST-1 and impregnated Pd/HKUST-1-IM. It is found that the storage capacity variation is not only ascribed to the different textural properties of materials but is also illustrated by the hydrogen spillover induced by different electron transport from Pd to the pores of MOFs (Pd@HKUST-1-DS > Pd/HKUST-1-IM), based on X-ray photoelectron spectroscopy and temperature desorption spectra. Pd@HKUST-1-DS, featuring high specific surface area, uniform Pd NP dispersion and strong interaction of Pd with hydrogen in the confined pore spaces of the support, displays the high hydrogen storage capacity. This work highlights the influence of spillover caused by Pd electron transport on the hydrogen storage capacity of metal NPs/MOFs, which is governed by both physical and chemical adsorption.
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