Systematic Search of Suitable Metal–Organic Frameworks for Thermal Energy-Storage Applications with Low Global Warming Potential Refrigerants

García, Edder J.; Bahamón, Daniel; Vega, Lourdes F.

doi:10.1021/acssuschemeng.0c07797

Cited by 21 publications

(11 citation statements)

References 84 publications

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“…In solar collectors, a wide ranges of functioning fluids have been tested. Historically, water, grease, ethylene glycol, and various lubricants have been used to promote the performance of solar collectors, as shown in Figure 2 [44][45][46][47][48][49]. In anchor fluids (water, ethylene glycol or oil/lubricant), nanosized metals (Al, Cu, Zn, Ag, Au, etc.…”

Section: Historical Backgroundmentioning

confidence: 99%

A Comprehensive Review on Efficiency Enhancement of Solar Collectors Using Hybrid Nanofluids

et al. 2022

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Because of its potential to directly transform solar energy into heat and energy, without harmful environmental effects such as greenhouse gas emissions. Hybrid nanofluid is an efficient way to improve the thermal efficiency of solar systems using a possible heat transfer fluid with superior thermo-physical properties. The object of this paper is the study the latest developments in hybrid applications in the fields of solar energy systems in different solar collectors. Hybrid nanofluids are potential fluids with better thermo-physical properties and heat transfer efficiency than conventional heat transfer fluids (oil, water, ethylene glycol) with single nanoparticle nanofluids. The research found that a single nanofluid can be replaced by a hybrid nanofluid because it enhances heat transfer. This work presented the recent developments in hybrid nanofluid preparation methods, stability factors, thermal improvement methods, current applications, and some mathematical regression analysis which is directly related to the efficiency enhancement of solar collector. This literature revealed that hybrid nanofluids have a great opportunity to enhance the efficiency of solar collector due to their noble thermophysical properties in replace of conventional heat transfer working fluids. Finally, some important problems are addressed, which must be solved for future study.

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Section: Historical Backgroundmentioning

confidence: 99%

A Comprehensive Review on Efficiency Enhancement of Solar Collectors Using Hybrid Nanofluids

et al. 2022

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“…For instance, Raabe and co-workers − employed classical molecular simulations to predict the thermophysical properties of pure HFOs and HCFOs, for which few experimental data were available. Recently, García et al performed a computational screening of 40 experimentally available metal–organic frameworks (MOFs) exploring their application in adsorption air-conditioning with selected low-GWP refrigerants, searching for the optimal MOF–refrigerant pair. The computational study provides useful guidance on the MOF topology, composition, and pore sizes needed to achieve the best performance with the selected low-GWP refrigerants for this application.…”

Section: Introductionmentioning

confidence: 99%

Searching for Sustainable Refrigerants by Bridging Molecular Modeling with Machine Learning

Alkhatib

Albà

Darwish

et al. 2022

Ind. Eng. Chem. Res.

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We present here a novel integrated approach employing machine learning algorithms for predicting thermophysical properties of fluids. The approach allows obtaining molecular parameters to be used in the polar soft-statistical associating fluid theory (SAFT) equation of state using molecular descriptors obtained from the conductor-like screening model for real solvents (COSMO-RS). The procedure is used for modeling 18 refrigerants including hydrofluorocarbons, hydrofluoroolefins, and hydrochlorofluoroolefins. The training dataset included six inputs obtained from COSMO-RS and five outputs from polar soft-SAFT parameters, with the accurate algorithm training ensured by its high statistical accuracy. The predicted molecular parameters were used in polar soft-SAFT for evaluating the thermophysical properties of the refrigerants such as density, vapor pressure, heat capacity, enthalpy of vaporization, and speed of sound. Predictions provided a good level of accuracy (AADs = 1.3–10.5%) compared to experimental data, and within a similar level of accuracy using parameters obtained from standard fitting procedures. Moreover, the predicted parameters provided a comparable level of predictive accuracy to parameters obtained from standard procedure when extended to modeling selected binary mixtures. The proposed approach enables bridging the gap in the data of thermodynamic properties of low global warming potential refrigerants, which hinders their technical evaluation and hence their final application.

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“…A few representative examples are provided by gas-gas separation (D 2 /H 2 16 , O 2 /N 2 17 , CO/N 2 18 , CO 2 /H 2 19 , ethane/ethylene 20 , and other gas mixtures 21 ), the enantioselectivity of chemical compounds 22 , gas adsorption (CO 2 23 , CH 4 24 , H 2 25 , thiol 26 , organosulfurs 27 , and acetylene 28 ), and combinations thereof 29,30 . Several computational explorations of MOFs datasets have been carried out also for biomedical (drug delivery 31 ), mechanical (CO 2 Brayton cycle 32 and osmotic heat engine 33 ), and energy applications (heat pumps/chillers 34,35 and thermal energy storage 36 ).…”

Section: Introductionmentioning

confidence: 99%

Minimal crystallographic descriptors of sorption properties in hypothetical MOFs and role in sequential learning optimization

et al. 2022

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We focus on gas sorption within metal-organic frameworks (MOFs) for energy applications and identify the minimal set of crystallographic descriptors underpinning the most important properties of MOFs for CO2 and H2O. A comprehensive comparison of several sequential learning algorithms for MOFs properties optimization is performed and the role played by those descriptors is clarified. In energy transformations, thermodynamic limits of important figures of merit crucially depend on equilibrium properties in a wide range of sorbate coverage values, which is often only partially accessible, hence possibly preventing the computation of desired objective functions. We propose a fast procedure for optimizing specific energy in a closed sorption energy storage system with only access to a single water Henry coefficient value and to the specific surface area. We are thus able to identify hypothetical candidate MOFs that are predicted to outperform state-of-the-art water-sorbent pairs for thermal energy storage applications.

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Systematic Search of Suitable Metal–Organic Frameworks for Thermal Energy-Storage Applications with Low Global Warming Potential Refrigerants

Cited by 21 publications

References 84 publications

A Comprehensive Review on Efficiency Enhancement of Solar Collectors Using Hybrid Nanofluids

A Comprehensive Review on Efficiency Enhancement of Solar Collectors Using Hybrid Nanofluids

Searching for Sustainable Refrigerants by Bridging Molecular Modeling with Machine Learning

Minimal crystallographic descriptors of sorption properties in hypothetical MOFs and role in sequential learning optimization

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