Adsorption heat pumps (AHPs) powered by low-grade waste heat or renewable energy can reduce electricity consumption and carbon emission. The exploration of the high-performing adsorbents of AHPs is the key...
Developing
materials with outstanding performance for sorption
thermal energy storage (STES) is vital in utilizing renewable energy.
Metal–organic frameworks (MOFs) and covalent organic frameworks
(COFs) have attracted much interest for application in STES due to
their excellent adsorption properties, including large capacities
and stepwise adsorption isotherms. However, the energy density (Q
ed), an essential property to look at when choosing
a suitable material for STES, is still elusive due to the different
composition methods in the experiment. This work evaluated and compared
the material-based Q
ed’s of MOFs
and COFs for STES via grand canonical Monte Carlo simulations. It
was demonstrated that most MOFs exhibited larger Q
ed than COFs since MOFs tend to have high loading during
the charging process. Nevertheless, it was found that one COF exhibited
the highest Q
ed ascribed to the low density
and complete desorption during the discharging process, which suggested
that COFs can possess excellent performance as long as they achieve
sufficient capacity during the charging process. Moreover, the structure–property
relationship indicated that large pore volume, relatively small density,
suitable carbon atom ratio, and isotropic 3D cage were favorable for
large-Q
ed structures. The successful implementation
of data mining and machine learning algorithms paves the way for rational
design and speeds up the assessment of the Q
ed of nanoporous materials.
Capturing formaldehydes (HCHO) from indoor air with porous adsorbents still faces challenges due to their low capacity and poor selectivity. Metal-organic frameworks (MOFs) with tunable pore properties were regarded as promising adsorbents for HCHO removal. However, the water presence in humid air heavily influences the formaldehyde capture performance due to the competition adsorption. To find suitable MOFs for formaldehyde capture and explore the relationship between MOFs structure and performance both in dry air and humid air, we performed grand canonical Monte Carlo (GCMC) molecular simulations to obtain working capacity and selectivity that evaluated the HCHO capture performance of MOFs without humidity. The results reveal that small pore size (~5 Å) and moderate heat of adsorption (40–50 kJ/mol) are favored for HCHO capture without water. It was found that the structure with a 3D cage instead of a 2D channel benefits the HCHO adsorption. Atoms in these high-performing MOFs should possess relatively small charges, and large Lennard-jones parameters were also preferred. Furthermore, it was indicated that Henry’s constant (KH) can reflect the HCHO adsorption performance without humidity, in which the optimal range is 10−2–101. Hence, Henry’s constant selectivity of HCHO over water (SKH HCHO/H2O) and HCHO over mixture components (H2O, N2, and O2) was obtained to screen MOFs at an 80% humidity condition. It was suggested that SKH for the mixture component overestimates the influence of N2 and O2, in which the top structures absorb a quantity of water in GCMC simulation, while SKH HCHO/H2O can efficiently find high-performing MOFs for HCHO capture at humidity in low adsorption pressure. The ECATAT found in this work has 0.64 mol/kg working capacity, and barely adsorbs water during 0–1 bar, which is the promising candidate MOF for HCHO capture.
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